From 814534c274151b1fdacf48e4d3527264d49d1cff Mon Sep 17 00:00:00 2001
From: zxymark221 <zxymark@vt.edu>
Date: Tue, 20 Feb 2024 14:49:02 -0700
Subject: [PATCH 01/17] rllib update: front page update.

---
 languages/python/openai_rllib/README.md | 58 +++++++++++++++----------
 1 file changed, 34 insertions(+), 24 deletions(-)

diff --git a/languages/python/openai_rllib/README.md b/languages/python/openai_rllib/README.md
index da50a0c2b..1032a0195 100644
--- a/languages/python/openai_rllib/README.md
+++ b/languages/python/openai_rllib/README.md
@@ -1,51 +1,63 @@
 ## Create Anaconda environment
 
-At first, create an Anaconda environment that you will use for all your experiments. For your convenience, simply follow the next steps:
+Follow the following steps to create an Anaconda environment for this experiment:
 
-### 1<sup>st</sup> step: Log in on Eagle
+### 1st step: Log in on Kestrel (Can be skipped if work on local computer)
 
-Login on Eagle with:
+Login on Kestrel with:
 ```
-ssh eagle
+ssh kestrel
 ```
-or
+if you have hostname configured, or
 ```
-ssh <username>@eagle.hpc.nrel.gov
+ssh <username>@kestrel.hpc.nrel.gov
 ```
 
-### 2<sup>nd</sup> step: Set up Anaconda environment
+### 2nd step: Set up Anaconda environment
 
-The repo [provides](https://github.com/erskordi/HPC/blob/HPC-RL/languages/python/openai_rllib/env_example.yml) the `env_example.yml` file. Use it to create a new Anaconda environment at a directory of your choosing. There are three main directories on Eagle where you can install the new environment, namely `/home`, `/scratch`, and `/projects`. Please go to [NREL HPC resources page](https://nrel.github.io/HPC/) to find more information about [the various Eagle directories](https://nrel.github.io/HPC/languages/python/NREL_python.html) and [how to create new Anaconda environments](https://nrel.github.io/HPC/languages/python/conda.html).
+To use `conda` on Kestrel (different from Eagle), the Anaconda module needs to be loaded.
+```
+module purge
+module load anaconda3
+```
+
+We suggest creating a conda environment on your `\projects` rather than `\home` or `\scratch`. (#TODO: Check this with HPC team.)
 
 ***Example:***
 
-Begin by createing a subdirectory `/scratch/$USER/github-repos/`, `cd` there and clone the repo. Assuming you want to install your new environment in your `scratch` directory, you may want to create a directory that will contain all your Anaconda environments, e.g. `/scratch/$USER/conda-envs/`:
+Use the following script to create a conda environment:
 ```
-conda env create --prefix=/scratch/$USER/conda-envs/myenv -f env_example.yml
+conda create --prefix=/projects/$HPC_HANDLE/$USER/conda_envs/rl_hpc python=3.10
 ```
 
-### 3<sup>rd</sup> step: Run OpenAI Gym on a single node/single core
+Here, `$HPC_HANDLE` is the project handle and `$USER` is your HPC user name.
 
-After installation is complete, make sure everything is working correctly. You can test your installation by running a small example using one of the standard Gym environments (e.g. `CartPole-v0`).
+#TODO: adding the script installing necessary packages.
+
+### 3rd step: Test OpenAI Gym API
+
+After installation is complete, make sure everything is working correctly. You can test your installation by running a small example using one of the standard Gym environments (e.g. `CartPole-v1`).
 
 Activate the enironment and start a Python session
 ```
 module purge
-conda activate /scratch/$USER/conda-envs/myenv
+module load anaconda3
+conda activate /projects/$HPC_HANDLE/$USER/conda_envs/rl_hpc
 python
 ```
-Request an interactive session on Eagle, and then, run the following:
+Request an interactive session on Kestrel, and then, run the following:
 ```python
-import gym
+import gymnasium as gym
 
-env = gym.make("CartPole-v0")
-env.reset()
+env = gym.make("CartPole-v1")
+obs, info = env.reset()
 
 done = False
 
 while not done:
     action = env.action_space.sample()
-    obs, rew, done, _ = env.step(action)
+    obs, rew, terminated, truncated, info = env.step(action)
+    done = (terminated or truncated)
     print(action, obs, rew, done)
 ```
 If everything works correctly, you will see an output similar to:
@@ -66,13 +78,11 @@ If everything works correctly, you will see an output similar to:
 0 [-0.18972559 -0.81966549  0.1994578   1.38158021] 1.0 False
 0 [-0.2061189  -1.0166379   0.22708941  1.72943365] 1.0 True
 ```
-*Note that the above process does not involve any training.*
-
-### Install more packages
 
-Later, when you will start running reinforcement learning examples on Eagle, you will need to install other packages, most important of which the `Ray RLlib` library. This will enable you to run multiple instances of Gym in parallel over multiple cores per node, or even multiple nodes. You can always install new packages via:
+### 4th step: Test other libraries
+The following libraries should also be imported without an error.
 
 ```
-conda install -c conda-forge <package_name>
-pip install <package_name>
+import ray
+import torch
 ```

From 6aa7432dacb3d26545a37367613603ac4bef2ef9 Mon Sep 17 00:00:00 2001
From: zxymark221 <zxymark@vt.edu>
Date: Wed, 21 Feb 2024 12:37:20 -0700
Subject: [PATCH 02/17] rllib update: add a new customized gym environment and
 the corresponding training script.

---
 .../__pycache__/custom_env.cpython-37.pyc     | Bin 907 -> 0 bytes
 .../openai_rllib/custom_gym_env/custom_env.py | 284 +++++++++++++++++-
 .../openai_rllib/train/config_parser.py       |  91 ++++++
 .../python/openai_rllib/train/train_script.py |  92 ++++++
 4 files changed, 452 insertions(+), 15 deletions(-)
 delete mode 100644 languages/python/openai_rllib/custom_gym_env/__pycache__/custom_env.cpython-37.pyc
 create mode 100644 languages/python/openai_rllib/train/config_parser.py
 create mode 100644 languages/python/openai_rllib/train/train_script.py

diff --git a/languages/python/openai_rllib/custom_gym_env/__pycache__/custom_env.cpython-37.pyc b/languages/python/openai_rllib/custom_gym_env/__pycache__/custom_env.cpython-37.pyc
deleted file mode 100644
index 29bc0f11a8666bc62848f9f1065d7355be9af0a2..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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diff --git a/languages/python/openai_rllib/custom_gym_env/custom_env.py b/languages/python/openai_rllib/custom_gym_env/custom_env.py
index 332cb0640..9f63057cd 100644
--- a/languages/python/openai_rllib/custom_gym_env/custom_env.py
+++ b/languages/python/openai_rllib/custom_gym_env/custom_env.py
@@ -1,24 +1,278 @@
-import gym
+"""This is a very simple version of the car passing problem."""
 
-class BasicEnv(gym.Env):
+__author__ = "Xiangyu Zhang"
+__email__ = "Xiangyu.Zhang@nrel.gov"
+
+import gymnasium as gym
+from gymnasium import spaces
+import numpy as np
+
+TIME_LAPSE_REWARD = -1.0
+COLLISION_OOB_REWARD = -100.0
+TARGET_REACHED_REWARD = 200.0
+MAX_STEP_CNT = 50
+DEG2RAD = np.pi / 180
+
+
+class CarPassEnv(gym.Env):
+    """ This is a toy car passing problem: one car stays still in front of the
+        other. The agent will control the hind car to pass the stationary car 
+        and eventually reach to a point directly in front of the stationary
+        car. No physics such as inertia is considered (but can be easily
+        added). A car is represented using a circle, the moving car has radius 
+        of 1.5 and the stationary car has radius between 0.8 and 1.8 (the size 
+        of the car changes). Two car collide into each other when the circle 
+        overlaps. The coordinate of the destination is fixed at (2, 5) while 
+        the center of the stationary car is at (2, 0).
+
+        Observation:
+        Type: Box(2)
+        Num   Observation     Min     Max
+        0     Position X      -4      4
+        1     Position Y      -15     6
+
+        Actions:
+        Type: Box(2)
+        Num   Variable        Min     Max
+        0     Speed           0       3.0
+        1     Direction       0       360.0
+
+        Rewards:
+        Type                  Value       Description
+        Time lapse            -1          Represent the elapse of time, so the 
+                                          task will be finished asap.
+        Collision/OOB         -100        Two cars collide or the moving car is
+                                          out of boundary, both end the game.
+        Reached Destimation   200         The moving car arrives at the 
+                                          designated spot.
+    """
+
+    metadata = {
+        'render.modes': ['human', 'rgb_array'],
+        'video.frames_per_second': 50
+    }
 
     def __init__(self):
-        self.action_space = gym.spaces.Discrete(5)
-        self.observation_space = gym.spaces.Discrete(2)
-    
-    def reset(self):
-        state = 0
-        return state
-    
+        
+        self.target_position = [2.0, 5.0]
+        self.stationary_car_poistion = [2.0, 0.0]
+        self.moving_car_size = 1.5
+        self.stationary_car_size = None  # will be initialized in environment.
+
+        self.state_upper_limit = np.array([4.0, 6.0])
+        self.state_lower_limit = np.array([-4.0, -14.0])
+
+        self.action_upper_limit = np.array([1.0, 1.0])
+        self.action_lower_limit = np.array([-1.0, -1.0])
+
+        self.action_space = spaces.Box(self.action_lower_limit, 
+                                       self.action_upper_limit, 
+                                       dtype=np.float32)
+        self.observation_space = spaces.Box(self.state_lower_limit, 
+                                            self.state_upper_limit, 
+                                            dtype=np.float32)
+
+        self.state = None
+        self.viewer = None
+        self.step_count = 0
+
     def step(self, action):
-        state = 1
+        """ Makes one move.
+
+        There are three steps:
+        Step 1: Calculate next position of the car.
+        Step 2: Check if collision or out of boundary happens.
+        Step 3: Check if target reached.
 
-        if action == 2:
-            reward = 1
+        There are three situations that ends the episode:
+        1. Two cars collide.
+        2. Moving car goes out of boundary.
+        3. Step count reaches 50.
+
+        Args:
+          action: A Numpy array: the first element is the speed and the second 
+            is direction in degree. Assuming the speed is normalized (from -1.0
+            to 1.0), action speed in the environment is from 0.0 to 3.0.
+        
+        Returns:
+          state: A 2 Numpy array: the X-Y coordinates of the moving car.
+          reward: A float, numeric reward for the action taken.
+          terminated: A Boolean, indicating whether the episode has ended.
+          truncated: A Boolean, indicating if the episode has reached a maximum
+            length. See the following link for differences between terminated
+            and truncated: https://gymnasium.farama.org/tutorials/gymnasium_basics/handling_time_limits/
+          info: A dictionary.
+        """
+
+        self.step_count += 1
+        truncated = False
+        terminated = False
+
+        # Actions in this game is bounded. 
+        norm_speed = np.clip(action[0], -1.0, 1.0)  # only clip the speed.
+
+        speed = norm_speed * 1.5 + 1.5  # recover speed from normalized value.
+        direction = (action[1] * 180.0 + 180.0) * DEG2RAD
+        next_position = (self.state 
+                         + np.array([speed * np.sin(direction),
+                                     speed * np.cos(direction)]))
+
+        forward_move = (self.get_distance(self.state, 
+                                          self.target_position) 
+                        - self.get_distance(next_position, 
+                                            self.target_position))
+
+        self.state = next_position
+
+        two_cars_dist = self.get_distance(next_position,
+                                          self.stationary_car_poistion)
+        
+        if (two_cars_dist <= self.stationary_car_size + self.moving_car_size):
+            # Collide with stationary car
+            reward = COLLISION_OOB_REWARD
+            terminated = True
+        elif self.oob_detect(next_position):
+            # out of bound
+            reward = COLLISION_OOB_REWARD
+            terminated = True
+        elif self.get_distance(next_position, self.target_position) < 0.05:
+            # Reached target
+            reward = TARGET_REACHED_REWARD
+            terminated = True
         else:
-            reward = -1
+            # Went somewhere.
+            if forward_move > 0.0:
+                # This reward serves as a guidance, without it we will 
+                reward = forward_move
+            else:
+                # if moving backwards, penalize at least a TIME_LAPSE_REWARD 
+                # for wasting time.
+                reward = min(forward_move, TIME_LAPSE_REWARD)
+
+        # Need to clip it, otherwise, the observation is out of the legal
+        # bound, and RLLIB will complain.
+        self.state = np.clip(next_position, 
+                             self.state_lower_limit, 
+                             self.state_upper_limit)
+
+        if self.step_count >= MAX_STEP_CNT:
+            truncated = True
+
+        # Info is to pass some variables' values out, but we do not use it
+        # here.
+        info = {}
+
+        return self.state, reward, terminated, truncated, info
+
+    def oob_detect(self, position):
+        """ Vehicle out of bound detection.
+        """
+        oob_flag = (position[0] > self.state_upper_limit[0] 
+                    or position[1] > self.state_upper_limit[1] 
+                    or position[0] < self.state_lower_limit[0] 
+                    or position[1] < self.state_lower_limit[1])
         
-        done = True
+        return oob_flag
+
+    def get_distance(self, coord1, coord2):
+        """ Calculates the distance between two coordinates.
+        
+        Args:
+          coord1: A list with two float numbers, represents the X-Y coordinate.
+          coord2: A list with two float numbers, represents the X-Y coordinate.
+        
+        Returns:
+          dist: A float representing the distance between these coordinates.
+        """
+
+        dist = np.sqrt((coord1[0] - coord2[0]) ** 2 
+                       + (coord1[1] - coord2[1]) ** 2)
+        
+        return dist
+
+    def reset(self, seed=None, options={}):
+
+        super().reset(seed=seed)
+
+        if options is None:
+            options = {}
+
+        self.state = np.array([2.0, np.random.uniform(-12.0, -8.0)])
+        # Choose the size of the car parked ahead.
+        self.stationary_car_size = np.random.uniform(0.8, 1.8)
+        self.step_count = 0
+
         info = {}
+        
+        return self.state, info
+
+    def render(self, mode='human'):
+        """ Rendering visually.
+
+        This function is totally optional. No need to render if your 
+        environment has nothing to show visually.
+        """
+
+        screen_width = 240
+        screen_height = 600
+
+        world_width = 8.0  # Display from -4 to 4.
+        scale = screen_width / world_width
+
+        def cord_transform(position, scale):
+            x = (position[0] + 4.0) * scale
+            y = (position[1] + 14.0) * scale
+            return [x, y]
+
+        if self.viewer is None:
+            from gymnasium.envs.classic_control import rendering
+            self.viewer = rendering.Viewer(screen_width, screen_height)
+
+            self.moving_car = rendering.make_circle(1.5 * scale)
+            self.cartrans = rendering.Transform()
+            self.moving_car.add_attr(self.cartrans)
+            self.moving_car.set_color(0.0, 0.0, 0.0)
+            self.cartrans.set_translation(*cord_transform(self.state, scale))
+            self.viewer.add_geom(self.moving_car)
+
+            self.stationary_car = rendering.make_circle(
+                self.stationary_car_size * scale)
+            self.stationary_car.set_color(.8, .6, .4)
+            self.stationary_car.add_attr(
+                rendering.Transform(
+                    translation=tuple(
+                        cord_transform(self.stationary_car_poistion, scale))))
+            self.viewer.add_geom(self.stationary_car)
+
+            self.target_spot = rendering.make_circle(0.5 * scale)
+            self.target_spot.set_color(1.0, 0.0, 0.0)
+            self.target_spot.add_attr(
+                rendering.Transform(translation=tuple(
+                    cord_transform(self.target_position, scale))))
+            self.viewer.add_geom(self.target_spot)
+
+        if self.state is None:
+            return None
+
+        self.cartrans.set_translation(*cord_transform(self.state, scale))
+
+        return self.viewer.render(return_rgb_array=mode == 'rgb_array')
+
+
+if __name__ == "__main__":
+
+    env = CarPassEnv()
+
+    obs = env.reset()
+    done = False
+    episodic_reward = 0.0
+
+    while not done:
+        act = env.action_space.sample()
+        obs, reward, terminated, truncated, info = env.step(act)
+        done = (terminated or truncated)
+        episodic_reward += reward
+
+    print("Reward this episode is %f" % episodic_reward)
+    print("Steps this episode is %d" % env.step_count)
 
-        return state, reward, done, info
\ No newline at end of file
diff --git a/languages/python/openai_rllib/train/config_parser.py b/languages/python/openai_rllib/train/config_parser.py
new file mode 100644
index 000000000..cae9dc004
--- /dev/null
+++ b/languages/python/openai_rllib/train/config_parser.py
@@ -0,0 +1,91 @@
+import argparse
+
+
+def create_parser():
+
+    parser = argparse.ArgumentParser()
+
+    # Training Setup
+    parser.add_argument(
+        "--run", type=str, default="PPO",
+        help="The RLlib-registered algorithm to use."
+    )
+    parser.add_argument(
+        "--framework",
+        choices=["tf", "tf2", "torch"],
+        default="torch",
+        help="The DL framework specifier.",
+    )
+    parser.add_argument(
+        "--checkpoint-frequency", type=int, default=20,
+        help="How frequent the policy will be saved."
+    )
+    parser.add_argument(
+        "--checkpoint-to-save", type=int, default=3,
+        help="Number of best policies to save."
+    )
+    parser.add_argument(
+        "--worker-num", type=int, default=102,
+        help="Number of parallel workers."
+    )
+    parser.add_argument(
+        "--exp-note", type=str, default=None,
+        help="The experiment note will be used in the path for saving models."
+    )
+
+    # Ray Configuration
+    parser.add_argument(
+        "--as-test",
+        action="store_true",
+        help="Whether this script should be run as a test: --stop-reward must "
+        "be achieved within --stop-timesteps AND --stop-iters.",
+    )
+    parser.add_argument(
+        "--ip-head", type=str, default=None,
+        help="The IP address of the head node of the ray cluster."
+    )
+    parser.add_argument(
+        "--redis-password", type=str, default=None,
+        help="The password to connect to the ray cluster."
+    )
+    parser.add_argument(
+        "--no-tune",
+        action="store_true",
+        help="Run without Tune using a manual train loop instead. In this case,"
+        "use PPO without grid search and no TensorBoard.",
+    )
+    parser.add_argument(
+        "--local-mode",
+        action="store_true",
+        help="Init Ray in local mode for easier debugging.",
+    )
+
+    # Training stopping criteria
+    parser.add_argument(
+        '--run-hour', type=int, default=1
+    )
+    parser.add_argument(
+        "--stop-timesteps", type=int, default=8e7,
+        help="Number of timesteps to train."
+    )
+    parser.add_argument(
+        "--stop-reward", type=float, default=190,
+        help="Reward at which we stop training."
+    )
+    parser.add_argument(
+        "--stop-iters", type=int, default=80000,
+        help="Number of iterations to train."
+    )
+
+    # Algorithm hyperparameters (PPO)
+    parser.add_argument(
+        '--lr', type=float, default=1e-5
+    )
+    parser.add_argument(
+        '--train-batch-size', type=int, default=20000
+    )
+    parser.add_argument(
+        '--entropy-coeff', type=float, default=0.0
+    )
+
+    return parser
diff --git a/languages/python/openai_rllib/train/train_script.py b/languages/python/openai_rllib/train/train_script.py
new file mode 100644
index 000000000..dc912f5e2
--- /dev/null
+++ b/languages/python/openai_rllib/train/train_script.py
@@ -0,0 +1,92 @@
+"""
+This example is modified from the following repo.
+https://github.com/NREL/rlc4clr/blob/main/train/train_stg1.py
+"""
+
+
+import os
+import sys
+
+import ray
+
+from ray import air, tune
+from ray.rllib.utils.framework import try_import_torch
+from ray.rllib.utils.test_utils import check_learning_achieved
+from ray.tune.registry import get_trainable_cls
+from ray.tune.registry import register_env
+
+
+CURRENT_FILE_PATH = os.path.dirname(os.path.abspath(__file__))
+LOG_PATH = os.path.join(CURRENT_FILE_PATH, 'results')
+
+torch, nn = try_import_torch()
+
+if __name__ == "__main__":
+
+    from config_parser import create_parser
+
+    parser = create_parser()
+
+    args = parser.parse_args()
+    print(f"Running with following CLI options: {args}")
+
+    # Ray cluster initialization
+    if args.ip_head is not None:
+        ray.init(address=args.ip_head, 
+                 _redis_password=args.redis_password,
+                 local_mode=False)
+    else:
+        ray.init(local_mode=args.local_mode)
+
+    env_name = 'CarPass-v0'
+
+    def env_creator(config):
+
+        custom_env_dir = CURRENT_FILE_PATH.replace('train', 'custom_gym_env')
+        sys.path.append(os.path.dirname(custom_env_dir))
+
+        from custom_gym_env.custom_env import CarPassEnv
+        env = CarPassEnv()
+        return env
+    
+    register_env(env_name, env_creator)
+
+    config = (
+        get_trainable_cls(args.run)
+        .get_default_config()
+        .environment(env_name, env_config={})
+        .framework(args.framework)
+        .rollouts(num_rollout_workers=args.worker_num)
+        .training(train_batch_size=args.train_batch_size, 
+                  lr=args.lr,
+                  model={"fcnet_hiddens": [256, 256]})
+        # Use GPUs iff `RLLIB_NUM_GPUS` env var set to > 0.
+        .resources(num_gpus=int(os.environ.get("RLLIB_NUM_GPUS", "0")))
+    )
+
+    stop = {
+        # "training_iteration": args.stop_iters,
+        "timesteps_total": args.stop_timesteps,
+        "episode_reward_mean": args.stop_reward,
+    }
+
+    # automated run with Tune and grid search and TensorBoard
+    print("Training automatically with Ray Tune")
+    tuner = tune.Tuner(
+        args.run,
+        param_space=config.to_dict(),
+        run_config=air.RunConfig(
+            stop=stop, local_dir=LOG_PATH,
+            checkpoint_config=air.CheckpointConfig(
+                checkpoint_frequency=args.checkpoint_frequency,
+                num_to_keep=args.checkpoint_to_save,
+                checkpoint_score_attribute='sampler_results/episode_reward_mean'
+            )),
+    )
+    results = tuner.fit()
+
+    if args.as_test:
+        print("Checking if learning goals were achieved")
+        check_learning_achieved(results, args.stop_reward)
+
+    ray.shutdown()

From 0a66225a5cfa2be68135e7886cb15da91e40e60b Mon Sep 17 00:00:00 2001
From: zxymark221 <zxymark@vt.edu>
Date: Wed, 21 Feb 2024 16:27:22 -0700
Subject: [PATCH 03/17] rllib update: Adding kestrel sbatch scripts for
 training.

---
 languages/python/openai_rllib/train/README.md | 123 ++++++++++++++++++
 .../openai_rllib/train/train_multi_nodes.sh   |  61 +++++++++
 .../python/openai_rllib/train/train_script.py |   4 -
 .../openai_rllib/train/train_single_node.sh   |  18 +++
 4 files changed, 202 insertions(+), 4 deletions(-)
 create mode 100644 languages/python/openai_rllib/train/README.md
 create mode 100644 languages/python/openai_rllib/train/train_multi_nodes.sh
 create mode 100644 languages/python/openai_rllib/train/train_single_node.sh

diff --git a/languages/python/openai_rllib/train/README.md b/languages/python/openai_rllib/train/README.md
new file mode 100644
index 000000000..1d87bae95
--- /dev/null
+++ b/languages/python/openai_rllib/train/README.md
@@ -0,0 +1,123 @@
+## Training RL policy on Kestrel
+
+This directory contains examples of using RLlib for RL policy training.
+
+### Files in this directory
+
+`train_script.py`: The main python file that sets up and initiates the training process. **It not only works on Kestrel, but can be used for local training if you want to test on your local computer.**
+
+`config_parser.py`: Use to create an argparser with the options to customize training hyperparameters. Default values are also set within the file.
+
+`train_single_node.sh`: The SLURM sbatch script for job submission. This script shows an example of using a single computing node for training.
+
+`train_multi_nodes.sh`: The SLURM sbatch script for job submission. This script shows an example of using multiple Kestrel nodes for training.
+
+### Main training script
+
+Here we walk though the code in `train_script.py` to help the readers understand what each part of the code is doing.
+
+Firstly, loading configuration arguments for this training experiment.
+
+```
+from config_parser import create_parser
+parser = create_parser()
+args = parser.parse_args()
+
+print(f"Running with following CLI options: {args}")
+```
+
+This allows setting different configuration values in the SLURM sbatch script for the training: `python train_script.py --worker-num 100`. See [`config_parser.py`](config_parser.py) for different configuration options.
+
+RLlib training relies on the distributed computing framework called [Ray](https://www.ray.io/), so a Ray cluster needs to be initialized.
+
+When the `ip_head` configuration is provided, we will connect to the Ray server at that address. This usually happens when using multiple nodes for training and the Ray cluser is started in [train_multi_nodes.sh](train_multi_nodes.sh). Otherwise, we start the Ray server using `ray.init(local_mode=args.local_mode)`, and this happens in single node training.
+
+```
+# Ray cluster initialization
+if args.ip_head is not None:
+    ray.init(address=args.ip_head, 
+             _redis_password=args.redis_password,
+             local_mode=False)
+else:
+    ray.init(local_mode=args.local_mode)
+```
+
+Register the Gym environment:
+```
+env_name = 'CarPass-v0'
+
+def env_creator(config):
+    ...
+    return env
+
+register_env(env_name, env_creator)
+```
+
+Setting up the learning configuration and stopping criteria for the training task.
+```
+config = (
+    get_trainable_cls(args.run)
+    .get_default_config()
+    .environment(env_name, env_config={})
+    .framework(args.framework)
+    .rollouts(num_rollout_workers=args.worker_num)
+    .training(train_batch_size=args.train_batch_size, 
+              lr=args.lr,
+              model={"fcnet_hiddens": [256, 256]})
+    # Use GPUs iff `RLlib_NUM_GPUS` env var set to > 0.
+    .resources(num_gpus=int(os.environ.get("RLlib_NUM_GPUS", "0")))
+)
+
+# Stopping criteria
+stop = {
+    # "training_iteration": args.stop_iters,
+    "timesteps_total": args.stop_timesteps,
+    "episode_reward_mean": args.stop_reward,
+}
+```
+
+Here, `args.run` could be a string such as 'PPO', 'A3C', etc., indicating which RL algorithms to be used, and `get_trainable_cls` goes and fetches the right trainable class according to `args.run`. `.environment(env_name, ...)` specifies which environment to train on. `args.framework` defines the ML framework to use, either Tensorflow or PyTorch, and we use PyTorch as an example here. `.rollouts()` allows setting the number of parallel workers. This number is usually limited to the number of CPU cores, since a Kestrel node has 104 cores, we can have 103 rollout workers (leaving one core for other tasks.) When using multiple nodes for training, the rollout worker number is calculated [here](train_multi_nodes.sh#L17). In `.training(...)`, hyperparameters of the RL algorithm can be set. The neural network architecture can also be defined: we are using a [256, 256] fully connected network, but RLlib allows the user to set a customized network as well, see an example of a pre-trained network [here](https://github.com/NREL/rlc4clr/blob/main/train/train_stg2.py#L178).
+
+The actual training starts with the following code snippet, by putting everything, e.g., RL algorithm, stopping criteria and other configurations, together.
+
+```
+tuner = tune.Tuner(
+    args.run,
+    param_space=config.to_dict(),
+    run_config=air.RunConfig(
+        stop=stop, local_dir=LOG_PATH,
+        checkpoint_config=air.CheckpointConfig(
+            checkpoint_frequency=args.checkpoint_frequency,
+            num_to_keep=args.checkpoint_to_save,
+            checkpoint_score_attribute='sampler_results/episode_reward_mean'
+        )),
+)
+results = tuner.fit()
+```
+There is an option to set up how model checkpoints are to be saved in the `checkpoint_config`. Within it, `checkpoint_frequency=x` is how frequent the policy model needs to be saved, e.g., every x iterations; `num_to_keep=y` indicates how many best checkpoints to be saved, i.e., the Top y performing policies; and `checkpoint_score_attribute` specifies using which attribute to evaluate the policies (and determine which ones are the "best".)
+
+### SLURM Sbatch scripts
+
+**NOTE: You will need to change the $HPC_HANDLE and $USER variable in the script to be able to run on Kestrel.**
+
+There are two files for submitting training jobs: one for _single node job_ and the other for _multi-node job_, they follow the following two or three steps.
+
+#### Step 1 Loading the conda env
+
+```
+module purge
+module load anaconda3
+conda activate /projects/$HPC_HANDLE/$USER/conda_envs/rl_hpc
+```
+
+#### Step 2 Starting Ray cluster (Only needed for multi-node case)
+
+Code snippet omitted here, see the [multi-node train script](train_multi_nodes.sh) for details. In general, this step will calculate the worker number, obtain node name list, and start the Ray cluster on the head node and all worker nodes.
+
+#### Step 3 Starting RL training
+
+Start RL training by calling the main training script. This is where some hyperparameters or configurations can be set: e.g., `--run PPO` as shown below.
+
+```
+python -u train_script.py --run PPO --redis-password $redis_password --worker-num $worker_num --ip-head $ip_head
+```
diff --git a/languages/python/openai_rllib/train/train_multi_nodes.sh b/languages/python/openai_rllib/train/train_multi_nodes.sh
new file mode 100644
index 000000000..a9f789a08
--- /dev/null
+++ b/languages/python/openai_rllib/train/train_multi_nodes.sh
@@ -0,0 +1,61 @@
+#!/bin/bash
+#SBATCH --account=$HPC_HANDLE
+#SBATCH --time=1:00:00
+#SBATCH --job-name=rl_train
+#SBATCH --nodes=10
+#SBATCH --partition=short
+#SBATCH --tasks-per-node=1
+
+# Step 1. Loading conda env
+module purge
+module load anaconda3
+conda activate /projects/$HPC_HANDLE/$USER/conda_envs/rl_hpc
+
+# Step 2. Starting Ray Cluster
+#node_num=2 # Must be one less that the total number of nodes
+node_num=$(( $SLURM_JOB_NUM_NODES - 1 ))
+worker_num=$(( $SLURM_JOB_NUM_NODES * $SLURM_CPUS_ON_NODE - 1 ))
+echo "node_num="$node_num
+echo "worker_num="$worker_num
+
+nodes=$(scontrol show hostnames $SLURM_JOB_NODELIST) # Getting the node names
+nodes_array=( $nodes )
+
+echo "nodes: "$nodes
+echo "nodes_array: "$nodes_array
+
+node1=${nodes_array[0]}
+echo "node1: "$node1
+
+ip_prefix=$(srun --nodes=1 --ntasks=1 -w $node1 hostname --ip-address) # Making address
+suffix=':6379'
+ip_head=$ip_prefix$suffix
+redis_password=$(uuidgen)
+echo "ip_prefix: "$ip_prefix
+echo "suffix: "$suffix
+echo "ip_head: "$ip_head
+echo "redis_password: "$redis_password
+
+# export ip_head # Exporting for latter access by trainer.py
+
+echo "starting head"
+srun --nodes=1 --ntasks=1 -w $node1 ray start --block --head --port=6379 --redis-password=$redis_password --temp-dir="/tmp/scratch/ray"& # Starting the head
+sleep 30
+
+echo "starting workers"
+for ((  i=1; i<=$node_num; i++ ))
+do
+  node2=${nodes_array[$i]}
+  echo "i=${i}, node2=${node2}"
+  srun --nodes=1 --ntasks=1 -w $node2 ray start --block --address=$ip_head --redis-password=$redis_password --temp-dir="/tmp/scratch/ray"& # Starting the workers
+  sleep 5
+done
+
+sleep 20
+
+# Step 3. Start Training
+echo "Start training"
+
+TIME=$(squeue -j $SLURM_JOB_ID -h --Format TimeLimit)
+
+python -u train_script.py --run PPO --redis-password $redis_password --worker-num $worker_num --ip-head $ip_head
diff --git a/languages/python/openai_rllib/train/train_script.py b/languages/python/openai_rllib/train/train_script.py
index dc912f5e2..f907bf6c2 100644
--- a/languages/python/openai_rllib/train/train_script.py
+++ b/languages/python/openai_rllib/train/train_script.py
@@ -24,9 +24,7 @@
 if __name__ == "__main__":
 
     from config_parser import create_parser
-
     parser = create_parser()
-
     args = parser.parse_args()
     print(f"Running with following CLI options: {args}")
 
@@ -39,7 +37,6 @@
         ray.init(local_mode=args.local_mode)
 
     env_name = 'CarPass-v0'
-
     def env_creator(config):
 
         custom_env_dir = CURRENT_FILE_PATH.replace('train', 'custom_gym_env')
@@ -48,7 +45,6 @@ def env_creator(config):
         from custom_gym_env.custom_env import CarPassEnv
         env = CarPassEnv()
         return env
-    
     register_env(env_name, env_creator)
 
     config = (
diff --git a/languages/python/openai_rllib/train/train_single_node.sh b/languages/python/openai_rllib/train/train_single_node.sh
new file mode 100644
index 000000000..1443a6b00
--- /dev/null
+++ b/languages/python/openai_rllib/train/train_single_node.sh
@@ -0,0 +1,18 @@
+#!/bin/bash
+#SBATCH --account=$HPC_HANDLE
+#SBATCH --time=4:00:00
+#SBATCH --job-name=rl_train
+#SBATCH --nodes=1
+#SBATCH --partition=short
+#SBATCH --tasks-per-node=1
+
+# Step 1. Loading conda env
+module purge
+module load anaconda3
+conda activate /projects/$HPC_HANDLE/$USER/conda_envs/rl_hpc
+
+# Step 2. Starting Ray cluster.
+# This step is skipped for the single node training.
+
+# Step 3. Starting RL training
+python -u train_script.py --run PPO

From 0ab3bd3395b1118ae72ae2e0c1941ff3101109d1 Mon Sep 17 00:00:00 2001
From: zxymark221 <zxymark@vt.edu>
Date: Wed, 21 Feb 2024 16:32:40 -0700
Subject: [PATCH 04/17] rllib update: rename the folder.

---
 languages/python/{openai_rllib => RLlib}/README.md  |   0
 .../{openai_rllib => RLlib}/Tensorboard/README.md   |   0
 .../custom_gym_env/README.md                        |   0
 .../custom_gym_env/custom_env.py                    |   0
 .../custom_gym_env/custom_env_trainer.py            |   0
 .../python/{openai_rllib => RLlib}/env_example.yml  |   0
 .../simple-example-gpu/README.md                    |   0
 .../simple-example-gpu/env_example_gpu.yml          |   0
 .../simple-example-gpu/env_example_optimized_tf.yml |   0
 .../simple-example-gpu/gpu_trainer.sh               |   0
 .../simple-example-gpu/gpu_trainer_optimized_tf.sh  |   0
 .../simple-example-gpu/simple_trainer.py            |   0
 .../simple-example/README.md                        |   0
 .../simple-example/images/ppo_rew_comparison.png    | Bin
 .../simple-example/multi_node_trainer.sh            |   0
 .../simple-example/simple_trainer.py                |   0
 .../simple-example/slurm-6800229.out                |   0
 .../python/{openai_rllib => RLlib}/train/README.md  |   0
 .../{openai_rllib => RLlib}/train/config_parser.py  |   0
 .../train/train_multi_nodes.sh                      |   0
 .../{openai_rllib => RLlib}/train/train_script.py   |   0
 .../train/train_single_node.sh                      |   0
 22 files changed, 0 insertions(+), 0 deletions(-)
 rename languages/python/{openai_rllib => RLlib}/README.md (100%)
 rename languages/python/{openai_rllib => RLlib}/Tensorboard/README.md (100%)
 rename languages/python/{openai_rllib => RLlib}/custom_gym_env/README.md (100%)
 rename languages/python/{openai_rllib => RLlib}/custom_gym_env/custom_env.py (100%)
 rename languages/python/{openai_rllib => RLlib}/custom_gym_env/custom_env_trainer.py (100%)
 rename languages/python/{openai_rllib => RLlib}/env_example.yml (100%)
 rename languages/python/{openai_rllib => RLlib}/simple-example-gpu/README.md (100%)
 rename languages/python/{openai_rllib => RLlib}/simple-example-gpu/env_example_gpu.yml (100%)
 rename languages/python/{openai_rllib => RLlib}/simple-example-gpu/env_example_optimized_tf.yml (100%)
 rename languages/python/{openai_rllib => RLlib}/simple-example-gpu/gpu_trainer.sh (100%)
 rename languages/python/{openai_rllib => RLlib}/simple-example-gpu/gpu_trainer_optimized_tf.sh (100%)
 rename languages/python/{openai_rllib => RLlib}/simple-example-gpu/simple_trainer.py (100%)
 rename languages/python/{openai_rllib => RLlib}/simple-example/README.md (100%)
 rename languages/python/{openai_rllib => RLlib}/simple-example/images/ppo_rew_comparison.png (100%)
 rename languages/python/{openai_rllib => RLlib}/simple-example/multi_node_trainer.sh (100%)
 rename languages/python/{openai_rllib => RLlib}/simple-example/simple_trainer.py (100%)
 rename languages/python/{openai_rllib => RLlib}/simple-example/slurm-6800229.out (100%)
 rename languages/python/{openai_rllib => RLlib}/train/README.md (100%)
 rename languages/python/{openai_rllib => RLlib}/train/config_parser.py (100%)
 rename languages/python/{openai_rllib => RLlib}/train/train_multi_nodes.sh (100%)
 rename languages/python/{openai_rllib => RLlib}/train/train_script.py (100%)
 rename languages/python/{openai_rllib => RLlib}/train/train_single_node.sh (100%)

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similarity index 100%
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rename to languages/python/RLlib/README.md
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From 7f28c52f0fe8858a25f0453d76a8a9a0b2140317 Mon Sep 17 00:00:00 2001
From: Xiangyu Zhang <xiangyu.zhang@nrel.gov>
Date: Thu, 29 Feb 2024 22:37:50 -0700
Subject: [PATCH 05/17] rllib update: modified the render().

---
 .../python/RLlib/custom_gym_env/custom_env.py | 104 ++++++++++++------
 1 file changed, 73 insertions(+), 31 deletions(-)

diff --git a/languages/python/RLlib/custom_gym_env/custom_env.py b/languages/python/RLlib/custom_gym_env/custom_env.py
index 9f63057cd..02c104e23 100644
--- a/languages/python/RLlib/custom_gym_env/custom_env.py
+++ b/languages/python/RLlib/custom_gym_env/custom_env.py
@@ -49,7 +49,7 @@ class CarPassEnv(gym.Env):
 
     metadata = {
         'render.modes': ['human', 'rgb_array'],
-        'video.frames_per_second': 50
+        'render_fps': 4
     }
 
     def __init__(self):
@@ -73,7 +73,9 @@ def __init__(self):
                                             dtype=np.float32)
 
         self.state = None
-        self.viewer = None
+        self.screen = None
+        self.clock = None
+        self.render_mode = 'human'
         self.step_count = 0
 
     def step(self, action):
@@ -211,6 +213,8 @@ def render(self, mode='human'):
 
         This function is totally optional. No need to render if your 
         environment has nothing to show visually.
+
+        Modified from gymnasium cartpole example.
         """
 
         screen_width = 240
@@ -223,40 +227,75 @@ def cord_transform(position, scale):
             x = (position[0] + 4.0) * scale
             y = (position[1] + 14.0) * scale
             return [x, y]
-
-        if self.viewer is None:
-            from gymnasium.envs.classic_control import rendering
-            self.viewer = rendering.Viewer(screen_width, screen_height)
-
-            self.moving_car = rendering.make_circle(1.5 * scale)
-            self.cartrans = rendering.Transform()
-            self.moving_car.add_attr(self.cartrans)
-            self.moving_car.set_color(0.0, 0.0, 0.0)
-            self.cartrans.set_translation(*cord_transform(self.state, scale))
-            self.viewer.add_geom(self.moving_car)
-
-            self.stationary_car = rendering.make_circle(
-                self.stationary_car_size * scale)
-            self.stationary_car.set_color(.8, .6, .4)
-            self.stationary_car.add_attr(
-                rendering.Transform(
-                    translation=tuple(
-                        cord_transform(self.stationary_car_poistion, scale))))
-            self.viewer.add_geom(self.stationary_car)
-
-            self.target_spot = rendering.make_circle(0.5 * scale)
-            self.target_spot.set_color(1.0, 0.0, 0.0)
-            self.target_spot.add_attr(
-                rendering.Transform(translation=tuple(
-                    cord_transform(self.target_position, scale))))
-            self.viewer.add_geom(self.target_spot)
+        
+        def draw_circle(x, y, radius, color):
+            gfxdraw.aacircle(self.surf, x, y, radius, color)
+            gfxdraw.filled_circle(self.surf, x, y, radius, color)
+        
+        try:
+            import pygame
+            from pygame import gfxdraw
+        except ImportError as e:
+            print("pygame is not installed")
+            print("run `pip install pygame==2.5.2`")
+            raise ImportError
+
+        if self.screen is None:
+            pygame.init()
+            if self.render_mode == "human":
+                pygame.display.init()
+                self.screen = pygame.display.set_mode(
+                    (screen_width, screen_height)
+                )
+            else:  # mode == "rgb_array"
+                self.screen = pygame.Surface((screen_width, 
+                                              screen_height))
+        if self.clock is None:
+            self.clock = pygame.time.Clock()
 
         if self.state is None:
             return None
 
-        self.cartrans.set_translation(*cord_transform(self.state, scale))
+        # Set background
+        self.surf = pygame.Surface((screen_width, screen_height))
+        self.surf.fill((255, 255, 255))
+
+        # Draw target
+        x, y = [int(i) for i in cord_transform(self.target_position, scale)]
+        draw_circle(x, y, int(0.5 * scale), (129, 132, 203))
+        
+        # Draw moving vehicle
+        x, y = [int(i) for i in cord_transform(self.state, scale)]
+        draw_circle(x, y, int(1.5 * scale), (0, 0, 0))
+        
+        # Draw stationary vehicle
+        x, y = [int(i) 
+                for i in cord_transform(self.stationary_car_poistion, scale)]
+        draw_circle(x, y, int(self.stationary_car_size * scale),
+                    (202, 152, 101))
+
+        self.surf = pygame.transform.flip(self.surf, False, True)
+        self.screen.blit(self.surf, (0, 0))
+
+        if self.render_mode == "human":
+            pygame.event.pump()
+            self.clock.tick(self.metadata["render_fps"])
+            pygame.display.flip()
+
+        elif self.render_mode == "rgb_array":
+            return np.transpose(
+                np.array(pygame.surfarray.pixels3d(self.screen)), 
+                axes=(1, 0, 2)
+            )
+
+    def close(self):
+        
+        if self.screen is not None:
+            import pygame
+            pygame.display.quit()
+            pygame.quit()
 
-        return self.viewer.render(return_rgb_array=mode == 'rgb_array')
+        return super().close()
 
 
 if __name__ == "__main__":
@@ -268,10 +307,13 @@ def cord_transform(position, scale):
     episodic_reward = 0.0
 
     while not done:
+        env.render()
         act = env.action_space.sample()
         obs, reward, terminated, truncated, info = env.step(act)
         done = (terminated or truncated)
         episodic_reward += reward
+    
+    env.render()
 
     print("Reward this episode is %f" % episodic_reward)
     print("Steps this episode is %d" % env.step_count)

From 670846d3ec023b3a46ca71edb94d36e4f438f8fe Mon Sep 17 00:00:00 2001
From: zxymark221 <zxymark@vt.edu>
Date: Fri, 1 Mar 2024 10:28:31 -0700
Subject: [PATCH 06/17] rllib update: normalize the env observation.

---
 .../python/RLlib/custom_gym_env/custom_env.py | 52 +++++++++++++------
 1 file changed, 36 insertions(+), 16 deletions(-)

diff --git a/languages/python/RLlib/custom_gym_env/custom_env.py b/languages/python/RLlib/custom_gym_env/custom_env.py
index 02c104e23..162a743ac 100644
--- a/languages/python/RLlib/custom_gym_env/custom_env.py
+++ b/languages/python/RLlib/custom_gym_env/custom_env.py
@@ -59,8 +59,10 @@ def __init__(self):
         self.moving_car_size = 1.5
         self.stationary_car_size = None  # will be initialized in environment.
 
-        self.state_upper_limit = np.array([4.0, 6.0])
-        self.state_lower_limit = np.array([-4.0, -14.0])
+        self.space_upper_limit = np.array([4.0, 6.0])
+        self.space_lower_limit = np.array([-4.0, -14.0])
+        self.state_upper_limit = np.array([1.0, 1.0])
+        self.state_lower_limit = np.array([-1.0, -1.0])
 
         self.action_upper_limit = np.array([1.0, 1.0])
         self.action_lower_limit = np.array([-1.0, -1.0])
@@ -73,6 +75,7 @@ def __init__(self):
                                             dtype=np.float32)
 
         self.state = None
+        self.moving_car_position = None
         self.screen = None
         self.clock = None
         self.render_mode = 'human'
@@ -115,16 +118,16 @@ def step(self, action):
 
         speed = norm_speed * 1.5 + 1.5  # recover speed from normalized value.
         direction = (action[1] * 180.0 + 180.0) * DEG2RAD
-        next_position = (self.state 
+        next_position = (self.moving_car_position 
                          + np.array([speed * np.sin(direction),
                                      speed * np.cos(direction)]))
 
-        forward_move = (self.get_distance(self.state, 
+        forward_move = (self.get_distance(self.moving_car_position, 
                                           self.target_position) 
                         - self.get_distance(next_position, 
                                             self.target_position))
 
-        self.state = next_position
+        self.moving_car_position = next_position
 
         two_cars_dist = self.get_distance(next_position,
                                           self.stationary_car_poistion)
@@ -153,7 +156,7 @@ def step(self, action):
 
         # Need to clip it, otherwise, the observation is out of the legal
         # bound, and RLLIB will complain.
-        self.state = np.clip(next_position, 
+        self.state = np.clip(self.pos2state(), 
                              self.state_lower_limit, 
                              self.state_upper_limit)
 
@@ -169,10 +172,10 @@ def step(self, action):
     def oob_detect(self, position):
         """ Vehicle out of bound detection.
         """
-        oob_flag = (position[0] > self.state_upper_limit[0] 
-                    or position[1] > self.state_upper_limit[1] 
-                    or position[0] < self.state_lower_limit[0] 
-                    or position[1] < self.state_lower_limit[1])
+        oob_flag = (position[0] > self.space_upper_limit[0] 
+                    or position[1] > self.space_upper_limit[1] 
+                    or position[0] < self.space_lower_limit[0] 
+                    or position[1] < self.space_lower_limit[1])
         
         return oob_flag
 
@@ -199,14 +202,30 @@ def reset(self, seed=None, options={}):
         if options is None:
             options = {}
 
-        self.state = np.array([2.0, np.random.uniform(-12.0, -8.0)])
+        self.moving_car_position = np.array(
+            [2.0, np.random.uniform(-12.0, -8.0)])
         # Choose the size of the car parked ahead.
         self.stationary_car_size = np.random.uniform(0.8, 1.8)
         self.step_count = 0
 
+        self.state = self.pos2state()
+
         info = {}
         
         return self.state, info
+    
+    def pos2state(self):
+        """ Normalize the position to the range of [-1, 1] for better training.
+        """
+        x, y = self.moving_car_position
+        x_norm = x / self.space_upper_limit[0]
+
+        y_half_range = (self.space_upper_limit[1] 
+                        - self.space_lower_limit[1]) / 2
+        y_mean = (self.space_upper_limit[1] + self.space_lower_limit[1]) / 2
+        y_norm = (y - y_mean) / y_half_range
+
+        return x_norm, y_norm
 
     def render(self, mode='human'):
         """ Rendering visually.
@@ -253,7 +272,7 @@ def draw_circle(x, y, radius, color):
         if self.clock is None:
             self.clock = pygame.time.Clock()
 
-        if self.state is None:
+        if self.moving_car_position is None:
             return None
 
         # Set background
@@ -265,7 +284,8 @@ def draw_circle(x, y, radius, color):
         draw_circle(x, y, int(0.5 * scale), (129, 132, 203))
         
         # Draw moving vehicle
-        x, y = [int(i) for i in cord_transform(self.state, scale)]
+        x, y = [int(i) for i in cord_transform(self.moving_car_position,
+                                               scale)]
         draw_circle(x, y, int(1.5 * scale), (0, 0, 0))
         
         # Draw stationary vehicle
@@ -307,14 +327,14 @@ def close(self):
     episodic_reward = 0.0
 
     while not done:
-        env.render()
+        # env.render()
         act = env.action_space.sample()
         obs, reward, terminated, truncated, info = env.step(act)
+        print(obs)
         done = (terminated or truncated)
         episodic_reward += reward
     
-    env.render()
+    # env.render()
 
     print("Reward this episode is %f" % episodic_reward)
     print("Steps this episode is %d" % env.step_count)
-

From 186332d8638e973403d0865a6f28fc07d1c87edb Mon Sep 17 00:00:00 2001
From: zxymark221 <zxymark@vt.edu>
Date: Sun, 3 Mar 2024 10:24:01 -0700
Subject: [PATCH 07/17] rllib update: debug training to get successful
 learning.

---
 .../python/RLlib/custom_gym_env/custom_env.py   | 13 ++++++-------
 languages/python/RLlib/train/config_parser.py   |  9 ++++++---
 languages/python/RLlib/train/train_script.py    | 17 ++++++++++++-----
 3 files changed, 24 insertions(+), 15 deletions(-)

diff --git a/languages/python/RLlib/custom_gym_env/custom_env.py b/languages/python/RLlib/custom_gym_env/custom_env.py
index 162a743ac..5685e1a6e 100644
--- a/languages/python/RLlib/custom_gym_env/custom_env.py
+++ b/languages/python/RLlib/custom_gym_env/custom_env.py
@@ -7,10 +7,10 @@
 from gymnasium import spaces
 import numpy as np
 
-TIME_LAPSE_REWARD = -1.0
+TIME_LAPSE_REWARD = -10.0
 COLLISION_OOB_REWARD = -100.0
 TARGET_REACHED_REWARD = 200.0
-MAX_STEP_CNT = 50
+MAX_STEP_CNT = 25
 DEG2RAD = np.pi / 180
 
 
@@ -140,7 +140,7 @@ def step(self, action):
             # out of bound
             reward = COLLISION_OOB_REWARD
             terminated = True
-        elif self.get_distance(next_position, self.target_position) < 0.05:
+        elif self.get_distance(next_position, self.target_position) < 1.0:
             # Reached target
             reward = TARGET_REACHED_REWARD
             terminated = True
@@ -202,10 +202,9 @@ def reset(self, seed=None, options={}):
         if options is None:
             options = {}
 
-        self.moving_car_position = np.array(
-            [2.0, np.random.uniform(-12.0, -8.0)])
+        self.moving_car_position = np.array([2.0, -12.0])
         # Choose the size of the car parked ahead.
-        self.stationary_car_size = np.random.uniform(0.8, 1.8)
+        self.stationary_car_size = 1.0
         self.step_count = 0
 
         self.state = self.pos2state()
@@ -225,7 +224,7 @@ def pos2state(self):
         y_mean = (self.space_upper_limit[1] + self.space_lower_limit[1]) / 2
         y_norm = (y - y_mean) / y_half_range
 
-        return x_norm, y_norm
+        return np.array([x_norm, y_norm])
 
     def render(self, mode='human'):
         """ Rendering visually.
diff --git a/languages/python/RLlib/train/config_parser.py b/languages/python/RLlib/train/config_parser.py
index cae9dc004..c5fc6f2f6 100644
--- a/languages/python/RLlib/train/config_parser.py
+++ b/languages/python/RLlib/train/config_parser.py
@@ -69,7 +69,7 @@ def create_parser():
         help="Number of timesteps to train."
     )
     parser.add_argument(
-        "--stop-reward", type=float, default=190,
+        "--stop-reward", type=float, default=210,
         help="Reward at which we stop training."
     )
     parser.add_argument(
@@ -79,13 +79,16 @@ def create_parser():
 
     # Algorithm hyperparameters (PPO)
     parser.add_argument(
-        '--lr', type=float, default=1e-5
+        '--lr', type=float, default=5e-5
     )
     parser.add_argument(
-        '--train-batch-size', type=int, default=20000
+        '--train-batch-size', type=int, default=30000
     )
     parser.add_argument(
         '--entropy-coeff', type=float, default=0.0
     )
+    parser.add_argument(
+        '--policy-layers', nargs="+", type=int, default=None
+    )
 
     return parser
diff --git a/languages/python/RLlib/train/train_script.py b/languages/python/RLlib/train/train_script.py
index f907bf6c2..92b27dbc4 100644
--- a/languages/python/RLlib/train/train_script.py
+++ b/languages/python/RLlib/train/train_script.py
@@ -34,7 +34,7 @@
                  _redis_password=args.redis_password,
                  local_mode=False)
     else:
-        ray.init(local_mode=args.local_mode)
+        ray.init()
 
     env_name = 'CarPass-v0'
     def env_creator(config):
@@ -47,15 +47,21 @@ def env_creator(config):
         return env
     register_env(env_name, env_creator)
 
+    network_structure = (
+        args.policy_layers if args.policy_layers is not None
+        else [256, 256])
+
     config = (
         get_trainable_cls(args.run)
         .get_default_config()
         .environment(env_name, env_config={})
         .framework(args.framework)
         .rollouts(num_rollout_workers=args.worker_num)
-        .training(train_batch_size=args.train_batch_size, 
-                  lr=args.lr,
-                  model={"fcnet_hiddens": [256, 256]})
+        .training(train_batch_size=args.train_batch_size, lr=args.lr,
+                  vf_loss_coeff=1.,
+                #   entropy_coeff_schedule=[[0, 0.05], [4e5, 0.025], 
+                #                           [8e5, 0.001], [1.2e6, 0.0]],
+                  model={"fcnet_hiddens": network_structure})
         # Use GPUs iff `RLLIB_NUM_GPUS` env var set to > 0.
         .resources(num_gpus=int(os.environ.get("RLLIB_NUM_GPUS", "0")))
     )
@@ -72,7 +78,8 @@ def env_creator(config):
         args.run,
         param_space=config.to_dict(),
         run_config=air.RunConfig(
-            stop=stop, local_dir=LOG_PATH,
+            stop=stop, 
+            local_dir=os.path.join(LOG_PATH, args.run),
             checkpoint_config=air.CheckpointConfig(
                 checkpoint_frequency=args.checkpoint_frequency,
                 num_to_keep=args.checkpoint_to_save,

From f0a09c259be454efaf7a49041a6f72b18b26e940 Mon Sep 17 00:00:00 2001
From: Xiangyu Zhang <xiangyu.zhang@nrel.gov>
Date: Sun, 3 Mar 2024 22:28:11 -0700
Subject: [PATCH 08/17] rllib update: Add the rollout script, and a trained
 policy.

---
 .../python/RLlib/custom_gym_env/custom_env.py |   2 +-
 languages/python/RLlib/test/rollout.py        |  56 +++++
 .../checkpoint_000000/algorithm_state.pkl     | Bin 0 -> 60079 bytes
 .../policies/default_policy/policy_state.pkl  | Bin 0 -> 1622601 bytes
 .../default_policy/rllib_checkpoint.json      |   1 +
 .../checkpoint_000000/rllib_checkpoint.json   |   1 +
 .../checkpoint_000001/algorithm_state.pkl     | Bin 0 -> 60079 bytes
 .../policies/default_policy/policy_state.pkl  | Bin 0 -> 1622601 bytes
 .../default_policy/rllib_checkpoint.json      |   1 +
 .../checkpoint_000001/rllib_checkpoint.json   |   1 +
 ...ents.out.tfevents.1709475088.x1003c5s6b0n0 | Bin 0 -> 911528 bytes
 .../params.json                               | 224 ++++++++++++++++++
 .../params.pkl                                | Bin 0 -> 5332 bytes
 .../progress.csv                              |  31 +++
 .../result.json                               |  30 +++
 ...sic-variant-state-2024-03-03_07-07-35.json | Bin 0 -> 12210 bytes
 .../experiment_state-2024-03-03_07-07-35.json |  44 ++++
 17 files changed, 390 insertions(+), 1 deletion(-)
 create mode 100644 languages/python/RLlib/test/rollout.py
 create mode 100644 languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/checkpoint_000000/algorithm_state.pkl
 create mode 100644 languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/checkpoint_000000/policies/default_policy/policy_state.pkl
 create mode 100644 languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/checkpoint_000000/policies/default_policy/rllib_checkpoint.json
 create mode 100644 languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/checkpoint_000000/rllib_checkpoint.json
 create mode 100644 languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/checkpoint_000001/algorithm_state.pkl
 create mode 100644 languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/checkpoint_000001/policies/default_policy/policy_state.pkl
 create mode 100644 languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/checkpoint_000001/policies/default_policy/rllib_checkpoint.json
 create mode 100644 languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/checkpoint_000001/rllib_checkpoint.json
 create mode 100644 languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/events.out.tfevents.1709475088.x1003c5s6b0n0
 create mode 100644 languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/params.json
 create mode 100644 languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/params.pkl
 create mode 100644 languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/progress.csv
 create mode 100644 languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/result.json
 create mode 100644 languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/basic-variant-state-2024-03-03_07-07-35.json
 create mode 100644 languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/experiment_state-2024-03-03_07-07-35.json

diff --git a/languages/python/RLlib/custom_gym_env/custom_env.py b/languages/python/RLlib/custom_gym_env/custom_env.py
index 5685e1a6e..5b59c7c1c 100644
--- a/languages/python/RLlib/custom_gym_env/custom_env.py
+++ b/languages/python/RLlib/custom_gym_env/custom_env.py
@@ -49,7 +49,7 @@ class CarPassEnv(gym.Env):
 
     metadata = {
         'render.modes': ['human', 'rgb_array'],
-        'render_fps': 4
+        'render_fps': 2
     }
 
     def __init__(self):
diff --git a/languages/python/RLlib/test/rollout.py b/languages/python/RLlib/test/rollout.py
new file mode 100644
index 000000000..c2279f8bf
--- /dev/null
+++ b/languages/python/RLlib/test/rollout.py
@@ -0,0 +1,56 @@
+# Followed the example below.
+# https://github.com/ray-project/ray/blob/master/rllib/examples/sb2rllib_rllib_example.py
+
+import os
+import sys
+
+from ray.rllib.algorithms.ppo import PPO
+from ray.tune.registry import get_trainable_cls, register_env
+
+CURRENT_FILE_DIR = os.path.dirname(os.path.abspath(__file__))
+
+env_name = 'CarPass-v0'
+def env_creator(config):
+
+    custom_env_dir = CURRENT_FILE_DIR.replace('test', 'custom_gym_env')
+    sys.path.append(os.path.dirname(custom_env_dir))
+
+    from custom_gym_env.custom_env import CarPassEnv
+    env = CarPassEnv()
+    return env
+register_env(env_name, env_creator)
+
+# Use the same configuration as the one for training except the rollout worker
+# number, which can be limited at 1.
+config = (
+    get_trainable_cls("PPO")
+    .get_default_config()
+    .framework("torch")
+    .training(model={"fcnet_hiddens": [256, 256]})  # Same as the one in training
+    .rollouts(num_rollout_workers=1)
+)
+
+# Loading trained RL agent
+agent = PPO(config, env=env_name)
+checkpoint_folder = CURRENT_FILE_DIR.replace('test', 'train/results')
+trial_id = "PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/checkpoint_000001"
+checkpoint_path = os.path.join(checkpoint_folder, trial_id)
+agent.restore(checkpoint_path)
+print("agent restored.")
+
+# Rollout the trained agent.
+env = env_creator(None)
+state, _ = env.reset()
+terminated = False
+truncated = False
+total_rew = 0.0
+
+while not (terminated or truncated):
+    
+    act = agent.compute_single_action(state)
+    # print(act)
+    state, reward, terminated, truncated, info = env.step(act)
+    env.render()
+    total_rew += reward
+
+print(total_rew)
diff --git a/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/checkpoint_000000/algorithm_state.pkl b/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/checkpoint_000000/algorithm_state.pkl
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diff --git a/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/checkpoint_000000/policies/default_policy/rllib_checkpoint.json b/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/checkpoint_000000/policies/default_policy/rllib_checkpoint.json
new file mode 100644
index 000000000..652ed3365
--- /dev/null
+++ b/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/checkpoint_000000/policies/default_policy/rllib_checkpoint.json
@@ -0,0 +1 @@
+{"type": "Policy", "checkpoint_version": "1.1", "format": "cloudpickle", "state_file": "policy_state.pkl", "ray_version": "2.9.2", "ray_commit": "fce7a361807580953364e2da964f9498f3123bf9"}
\ No newline at end of file
diff --git a/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/checkpoint_000000/rllib_checkpoint.json b/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/checkpoint_000000/rllib_checkpoint.json
new file mode 100644
index 000000000..0f38c1099
--- /dev/null
+++ b/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/checkpoint_000000/rllib_checkpoint.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/checkpoint_000001/algorithm_state.pkl b/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/checkpoint_000001/algorithm_state.pkl
new file mode 100644
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diff --git a/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/checkpoint_000001/policies/default_policy/policy_state.pkl b/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/checkpoint_000001/policies/default_policy/policy_state.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..57f0ce5e9ba1a3544fd428099778bb159a561196
GIT binary patch
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diff --git a/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/checkpoint_000001/policies/default_policy/rllib_checkpoint.json b/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/checkpoint_000001/policies/default_policy/rllib_checkpoint.json
new file mode 100644
index 000000000..652ed3365
--- /dev/null
+++ b/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/checkpoint_000001/policies/default_policy/rllib_checkpoint.json
@@ -0,0 +1 @@
+{"type": "Policy", "checkpoint_version": "1.1", "format": "cloudpickle", "state_file": "policy_state.pkl", "ray_version": "2.9.2", "ray_commit": "fce7a361807580953364e2da964f9498f3123bf9"}
\ No newline at end of file
diff --git a/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/checkpoint_000001/rllib_checkpoint.json b/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/checkpoint_000001/rllib_checkpoint.json
new file mode 100644
index 000000000..fdd9c2396
--- /dev/null
+++ b/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/checkpoint_000001/rllib_checkpoint.json
@@ -0,0 +1 @@
+{"type": "Algorithm", "checkpoint_version": "1.1", "format": "cloudpickle", "state_file": "/tmp/tmp7g6hnta3/algorithm_state.pkl", "policy_ids": ["default_policy"], "ray_version": "2.9.2", "ray_commit": "fce7a361807580953364e2da964f9498f3123bf9"}
\ No newline at end of file
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diff --git a/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/params.json b/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/params.json
new file mode 100644
index 000000000..efe8f1658
--- /dev/null
+++ b/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/params.json
@@ -0,0 +1,224 @@
+{
+  "_AlgorithmConfig__prior_exploration_config": null,
+  "_disable_action_flattening": false,
+  "_disable_execution_plan_api": true,
+  "_disable_initialize_loss_from_dummy_batch": false,
+  "_disable_preprocessor_api": false,
+  "_enable_new_api_stack": false,
+  "_fake_gpus": false,
+  "_is_atari": null,
+  "_learner_class": null,
+  "_rl_module_spec": null,
+  "_tf_policy_handles_more_than_one_loss": false,
+  "action_mask_key": "action_mask",
+  "action_space": null,
+  "actions_in_input_normalized": false,
+  "algorithm_config_overrides_per_module": {},
+  "always_attach_evaluation_results": false,
+  "auto_wrap_old_gym_envs": true,
+  "batch_mode": "truncate_episodes",
+  "callbacks": "<class 'ray.rllib.algorithms.callbacks.DefaultCallbacks'>",
+  "checkpoint_trainable_policies_only": false,
+  "clip_actions": false,
+  "clip_param": 0.3,
+  "clip_rewards": null,
+  "compress_observations": false,
+  "count_steps_by": "env_steps",
+  "create_env_on_driver": false,
+  "custom_eval_function": null,
+  "custom_resources_per_worker": {},
+  "delay_between_worker_restarts_s": 60.0,
+  "disable_env_checking": false,
+  "eager_max_retraces": 20,
+  "eager_tracing": true,
+  "enable_async_evaluation": false,
+  "enable_connectors": true,
+  "enable_tf1_exec_eagerly": false,
+  "entropy_coeff": 0.0,
+  "entropy_coeff_schedule": null,
+  "env": "CarPass-v0",
+  "env_config": {},
+  "env_runner_cls": null,
+  "env_task_fn": null,
+  "evaluation_config": null,
+  "evaluation_duration": 10,
+  "evaluation_duration_unit": "episodes",
+  "evaluation_interval": null,
+  "evaluation_num_workers": 0,
+  "evaluation_parallel_to_training": false,
+  "evaluation_sample_timeout_s": 180.0,
+  "exploration_config": {
+    "type": "StochasticSampling"
+  },
+  "explore": true,
+  "export_native_model_files": false,
+  "extra_python_environs_for_driver": {},
+  "extra_python_environs_for_worker": {},
+  "fake_sampler": false,
+  "framework": "torch",
+  "gamma": 0.99,
+  "grad_clip": null,
+  "grad_clip_by": "global_norm",
+  "ignore_worker_failures": false,
+  "in_evaluation": false,
+  "input": "sampler",
+  "input_config": {},
+  "keep_per_episode_custom_metrics": false,
+  "kl_coeff": 0.2,
+  "kl_target": 0.01,
+  "lambda": 1.0,
+  "local_gpu_idx": 0,
+  "local_tf_session_args": {
+    "inter_op_parallelism_threads": 8,
+    "intra_op_parallelism_threads": 8
+  },
+  "log_level": "WARN",
+  "log_sys_usage": true,
+  "logger_config": null,
+  "logger_creator": null,
+  "lr": 5e-05,
+  "lr_schedule": null,
+  "max_num_worker_restarts": 1000,
+  "max_requests_in_flight_per_sampler_worker": 2,
+  "metrics_episode_collection_timeout_s": 60.0,
+  "metrics_num_episodes_for_smoothing": 100,
+  "min_sample_timesteps_per_iteration": 0,
+  "min_time_s_per_iteration": null,
+  "min_train_timesteps_per_iteration": 0,
+  "model": {
+    "_disable_action_flattening": false,
+    "_disable_preprocessor_api": false,
+    "_time_major": false,
+    "_use_default_native_models": -1,
+    "always_check_shapes": false,
+    "attention_dim": 64,
+    "attention_head_dim": 32,
+    "attention_init_gru_gate_bias": 2.0,
+    "attention_memory_inference": 50,
+    "attention_memory_training": 50,
+    "attention_num_heads": 1,
+    "attention_num_transformer_units": 1,
+    "attention_position_wise_mlp_dim": 32,
+    "attention_use_n_prev_actions": 0,
+    "attention_use_n_prev_rewards": 0,
+    "conv_activation": "relu",
+    "conv_filters": null,
+    "custom_action_dist": null,
+    "custom_model": null,
+    "custom_model_config": {},
+    "custom_preprocessor": null,
+    "dim": 84,
+    "encoder_latent_dim": null,
+    "fcnet_activation": "tanh",
+    "fcnet_hiddens": [
+      256,
+      256
+    ],
+    "framestack": true,
+    "free_log_std": false,
+    "grayscale": false,
+    "lstm_cell_size": 256,
+    "lstm_use_prev_action": false,
+    "lstm_use_prev_action_reward": -1,
+    "lstm_use_prev_reward": false,
+    "max_seq_len": 20,
+    "no_final_linear": false,
+    "post_fcnet_activation": "relu",
+    "post_fcnet_hiddens": [],
+    "use_attention": false,
+    "use_lstm": false,
+    "vf_share_layers": false,
+    "zero_mean": true
+  },
+  "normalize_actions": true,
+  "num_consecutive_worker_failures_tolerance": 100,
+  "num_cpus_for_driver": 1,
+  "num_cpus_per_learner_worker": 1,
+  "num_cpus_per_worker": 1,
+  "num_envs_per_worker": 1,
+  "num_gpus": 0,
+  "num_gpus_per_learner_worker": 0,
+  "num_gpus_per_worker": 0,
+  "num_learner_workers": 0,
+  "num_sgd_iter": 30,
+  "num_workers": 207,
+  "observation_filter": "NoFilter",
+  "observation_fn": null,
+  "observation_space": null,
+  "off_policy_estimation_methods": {},
+  "offline_sampling": false,
+  "ope_split_batch_by_episode": true,
+  "optimizer": {},
+  "output": null,
+  "output_compress_columns": [
+    "obs",
+    "new_obs"
+  ],
+  "output_config": {},
+  "output_max_file_size": 67108864,
+  "placement_strategy": "PACK",
+  "policies": {
+    "default_policy": [
+      null,
+      null,
+      null,
+      null
+    ]
+  },
+  "policies_to_train": null,
+  "policy_map_cache": -1,
+  "policy_map_capacity": 100,
+  "policy_mapping_fn": "<function AlgorithmConfig.DEFAULT_POLICY_MAPPING_FN at 0x7faf9cdda170>",
+  "policy_states_are_swappable": false,
+  "postprocess_inputs": false,
+  "preprocessor_pref": "deepmind",
+  "recreate_failed_workers": false,
+  "remote_env_batch_wait_ms": 0,
+  "remote_worker_envs": false,
+  "render_env": false,
+  "replay_sequence_length": null,
+  "restart_failed_sub_environments": false,
+  "rollout_fragment_length": "auto",
+  "sample_async": -1,
+  "sample_collector": "<class 'ray.rllib.evaluation.collectors.simple_list_collector.SimpleListCollector'>",
+  "sampler_perf_stats_ema_coef": null,
+  "seed": null,
+  "sgd_minibatch_size": 128,
+  "shuffle_buffer_size": 0,
+  "shuffle_sequences": true,
+  "simple_optimizer": -1,
+  "sync_filters_on_rollout_workers_timeout_s": 60.0,
+  "synchronize_filters": -1,
+  "tf_session_args": {
+    "allow_soft_placement": true,
+    "device_count": {
+      "CPU": 1
+    },
+    "gpu_options": {
+      "allow_growth": true
+    },
+    "inter_op_parallelism_threads": 2,
+    "intra_op_parallelism_threads": 2,
+    "log_device_placement": false
+  },
+  "torch_compile_learner": false,
+  "torch_compile_learner_dynamo_backend": "inductor",
+  "torch_compile_learner_dynamo_mode": null,
+  "torch_compile_learner_what_to_compile": "forward_train",
+  "torch_compile_worker": false,
+  "torch_compile_worker_dynamo_backend": "onnxrt",
+  "torch_compile_worker_dynamo_mode": null,
+  "train_batch_size": 30000,
+  "update_worker_filter_stats": true,
+  "use_critic": true,
+  "use_gae": true,
+  "use_kl_loss": true,
+  "use_worker_filter_stats": true,
+  "validate_workers_after_construction": true,
+  "vf_clip_param": 10.0,
+  "vf_loss_coeff": 1.0,
+  "vf_share_layers": -1,
+  "worker_cls": -1,
+  "worker_health_probe_timeout_s": 60,
+  "worker_restore_timeout_s": 1800
+}
\ No newline at end of file
diff --git a/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/params.pkl b/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/params.pkl
new file mode 100644
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diff --git a/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/progress.csv b/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/progress.csv
new file mode 100644
index 000000000..e4baa57bc
--- /dev/null
+++ b/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/progress.csv
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6, 6, 3, 5, 5, 7, 4, 6, 6, 4, 7, 8, 4, 8, 11, 8, 8, 6, 7, 12, 7, 2, 2, 7, 15, 4, 10, 13, 2, 4, 6, 11, 7, 1, 5, 14, 5, 7, 16, 4, 1, 2, 1, 5, 15, 8, 9, 1, 1, 5, 4, 4, 8, 4, 1, 4, 1, 10, 12, 12, 16, 3, 8, 10, 5, 5, 8, 7, 4, 1, 7, 4, 4, 5, 1, 6, 8, 1, 17, 2, 1, 5, 7, 6, 1, 4, 4, 7, 3, 5, 3, 3, 12, 4, 6, 12, 4, 6, 6, 2, 10, 4, 8, 6, 6, 5, 6, 2, 6, 1, 8, 3, 16, 2, 8, 8, 8, 6, 1, 1, 5, 2, 5, 1, 2, 1, 2, 6, 10, 17, 3, 1, 6, 14, 9, 3, 4, 4, 5, 1, 1, 4, 4, 6, 4, 9, 7, 1, 2, 1, 3, 4, 5, 4, 7, 11, 3, 10, 1, 5, 6, 3, 2, 1, 5, 9, 15, 2, 8, 1, 12, 2, 2, 5, 1, 10, 5, 7, 10, 1, 4, 8, 3, 6, 4, 1, 2, 2, 2, 8, 13, 16, 15, 3, 1, 1, 2, 1, 5, 5, 1, 4, 5, 1, 1, 6, 6, 6, 6, 2, 2, 5, 7, 20, 8, 3, 17, 4, 8, 16, 6, 4, 9, 5, 5, 10, 5, 7, 5, 11, 7, 9, 6, 3, 2, 7, 1, 17, 6, 8, 11, 8, 1, 5, 1, 6, 7, 9, 1, 16, 5, 14, 5, 25, 10, 1, 2, 7, 6, 6, 5, 5, 3, 8, 1, 1, 15, 5, 5, 3, 8, 4, 6, 13, 6, 5, 12, 9, 18, 3, 1, 3, 1, 21, 1, 8, 5, 5, 3, 12, 5, 2, 2, 1, 2, 1, 16, 3, 8, 10, 8, 1, 6, 4, 8, 4, 1, 4, 8, 2, 6, 5, 1, 7, 2, 1, 5, 1, 7, 5, 12, 5, 4, 15, 4, 21, 5, 14, 12, 9, 1, 8, 5, 7, 3, 7, 10, 3, 11, 5, 12, 5, 2, 3, 8, 10, 7, 6, 7, 4, 8, 5, 1, 4, 7, 9, 10, 2, 4, 5, 4, 4, 2, 5, 1, 4, 3, 4, 6, 1, 16, 7, 11, 4, 3, 2, 7, 3, 1, 5, 6, 8, 6, 5, 3, 5, 1, 3, 8, 1, 9, 3, 6, 5, 6, 5, 7, 6, 5, 13, 6, 12, 8, 1, 1, 4, 4, 5, 8, 1, 10, 7, 6, 6, 1, 3, 11, 6, 6, 7, 6, 6, 9, 6, 3, 12, 12, 4, 13, 3, 4, 5, 5, 1, 8, 4, 10, 3, 5, 4, 6, 3, 4, 7, 4, 6, 11, 4, 2, 1, 3, 23, 7, 10, 8, 13, 5, 1, 7, 7, 4, 11, 6, 4, 11, 5, 1, 5, 5, 6, 5, 4, 11, 5, 4, 14, 4, 2, 3, 13, 10, 11, 16, 4, 1, 7, 4, 4, 5, 1, 2, 8, 3, 9, 4, 3, 6, 7, 12, 1, 6, 4, 1, 5, 4, 7, 5, 1, 6, 3, 6, 4, 8, 5, 1, 8, 4, 4, 1, 4, 6, 5, 20, 10, 4, 4, 1, 6, 4, 5, 1, 8, 1, 6, 4, 16, 5, 9, 4, 6, 6, 6, 1, 5, 7, 1, 1, 3, 9, 2, 5, 4, 6, 5, 6, 10, 8, 16, 2, 4, 8, 9, 6, 7, 16, 15, 9, 8, 15, 3, 7, 4, 2, 5, 11, 4, 5, 16, 16, 6, 7, 6, 11, 7, 1, 6, 4, 5, 21, 1, 1, 16, 6, 10, 8, 4, 6, 5, 9, 15, 3, 15, 8, 13, 1, 16, 1, 12, 1, 6, 4, 5, 3, 3, 4, 4, 2, 8, 11, 1, 6, 7, 8, 5, 3, 8, 15, 4, 7, 6, 2, 3, 7, 6, 1, 5, 1, 3, 1, 7, 14, 1, 2, 4, 10, 1, 9, 5, 4, 10, 4, 10, 10, 6, 5, 1, 8, 1, 4, 3, 5, 3, 6, 6, 10, 4, 14, 8, 12, 2, 5, 1, 7, 8, 13, 1, 6, 6, 4, 1, 7, 8, 6, 5, 4, 6, 12, 4, 4, 17, 4, 12, 5, 11, 3, 1, 8, 7, 5, 6, 4, 1, 6, 5, 2, 8, 4, 1, 1, 1, 2, 6, 6, 7, 2, 5, 15, 5, 6, 5, 8, 1, 6, 1, 16, 6, 5, 5, 7, 3, 1, 1, 3, 1, 7, 16, 22, 2, 9, 3, 18, 6, 7, 7, 14, 4, 4, 5, 6, 12, 5, 1, 6, 3, 5, 10, 11, 6, 5, 2, 4, 3, 3, 4, 2, 5, 7, 8, 7, 16, 5, 6, 1, 4, 2, 5, 14, 4, 1, 5, 9, 6, 6, 7, 4, 11, 6, 14, 4, 13, 14, 6, 1, 3, 4, 4, 5, 7, 2, 9, 4, 8, 5, 4, 4, 5, 1, 7, 4, 1, 5, 5, 8, 1, 2, 9, 17, 5, 6, 6, 12, 2, 4, 4, 5, 15, 5, 11, 2, 4, 4, 1, 8, 5, 1, 5, 4, 8, 9, 14, 10, 1, 10, 3, 4, 5, 6, 4, 2, 6, 9, 5, 2, 2, 4, 11, 6, 8, 4, 1, 3, 5, 4, 9, 1, 1, 11, 2, 6, 6, 1, 9, 4, 4, 7, 10, 1, 5, 7, 6, 6, 4, 7, 4, 2, 1, 4, 3, 4, 1, 6, 5, 6, 8, 10, 9, 6, 14, 12, 7, 3, 7, 16, 2, 1, 6, 4, 11, 10, 10, 7, 6, 6, 4, 14, 5, 6, 8, 2, 9, 10, 7, 2, 7, 7, 7, 1, 5, 5, 3, 20, 4, 17, 1, 1, 4, 9, 18, 6, 14, 6, 2, 16, 1, 10, 8, 3, 6, 6, 1, 17, 25, 4, 2, 2, 1, 5, 4, 5, 6, 5, 2, 10, 9, 6, 1, 1, 4, 13, 13, 5, 12, 4, 6, 8, 1, 4, 6, 1, 4, 1, 4, 3, 3, 4, 5, 9, 6, 3, 1, 6, 7, 5, 4, 7, 13, 6, 4, 15, 1, 9, 2, 5, 4, 4, 1, 1, 3, 2, 4, 10, 3, 2, 10, 7, 4, 4, 1, 9, 3, 16, 8, 5, 9, 7, 12, 4, 19, 2, 5, 6, 4, 6, 5, 7, 1, 7, 14, 8, 7, 1, 9, 3, 1, 2, 22, 4, 1, 6, 7, 8, 1, 3, 4, 17, 4, 5, 15, 4, 3, 1, 13, 13, 4, 3, 1, 14, 1, 4, 11, 4, 3, 17, 8, 14, 6, 4, 5, 10, 6, 5, 4, 7, 4, 11, 7, 4, 14, 14, 18, 8, 11, 18, 7, 7, 6, 2, 6, 5, 4, 5, 6, 5, 20, 1, 5, 3, 9, 1, 12, 5, 3, 4, 4, 2, 6, 4, 12, 4, 6, 4, 2, 5, 1, 11, 10, 5, 5, 4, 1, 6, 10, 6, 12, 14, 5, 7, 2, 1, 2, 13, 2, 7, 6, 14, 1, 3, 6, 3, 9, 4, 9, 10, 1, 11, 2, 12, 6, 8, 5, 1, 5, 14, 5, 7, 8, 8, 8, 1, 13, 9, 5, 2, 9, 7, 6, 7, 1, 2, 2, 5, 5, 2, 6, 16, 8, 9, 6, 4, 8, 6, 6, 3, 10, 5, 14, 9, 1, 5, 8, 7, 3, 6, 3, 4, 3, 9, 8, 3, 9, 3, 6, 8, 6, 2, 4, 6, 2, 4, 4, 1, 4, 6, 2, 2, 1, 4, 4, 9, 2, 1, 11, 4, 6, 3, 5, 3, 12, 13, 5, 9, 5, 8, 6, 1, 6, 6, 1, 2, 5, 5, 6, 2, 10, 7, 5, 6, 5, 6, 7, 1, 5, 5, 16, 5, 5, 2, 1, 7, 7, 1, 1, 5, 2, 12, 5, 16, 1, 8, 1, 8, 7, 4, 6, 8, 4, 8, 9, 4, 5, 13, 5, 5, 2, 6, 5, 6, 4, 1, 13, 5, 10, 6, 1, 6, 11, 13, 1, 14, 4, 1, 2, 4, 1, 1, 5, 22, 3, 4, 2, 1, 7, 6, 2, 2, 15, 5, 5, 6, 5, 4, 4, 9, 1, 2, 6, 7, 6, 13, 1, 4, 13, 3, 6, 6, 3, 9, 1, 4, 3, 5, 2, 10, 11, 5, 4, 1, 8, 5, 5, 12, 4, 4, 1, 7, 5, 2, 2, 4, 8, 4, 3, 14, 2, 12, 16, 2, 8, 5, 1, 6, 4, 5, 11, 6, 2, 6, 5, 15, 10, 5, 1, 4, 11, 2, 2, 6, 4, 1, 5, 10, 1, 1, 22, 5, 4, 4, 21, 2, 19, 1, 10, 3, 12, 3, 3, 1, 7, 8, 10, 9, 5, 3, 7, 7, 1, 17, 7, 5, 7, 1, 14, 1, 6, 14, 4, 1, 2, 1, 2, 6, 5, 5, 1, 8, 1, 10, 6, 4, 6, 17, 4, 4, 6, 6, 4, 5, 8, 5, 4, 4, 5, 13, 1, 6, 5, 14, 1, 6, 5, 5, 2, 8, 2, 7, 2, 13, 8, 5, 2, 4, 4, 1, 4, 13, 5, 15, 5, 1, 10, 5, 2, 4, 2, 5, 2, 7, 4, 5, 4, 2, 2, 7, 4, 12, 4, 12, 5, 11, 5, 4, 1, 2, 2, 4, 8, 7, 5, 1, 8, 5, 5, 4, 1, 4, 7, 18, 6, 1, 12, 8, 10, 2, 2, 6, 5, 2, 6, 8, 5, 5, 1, 5, 6, 4, 1, 1, 5, 4, 5, 8, 4, 6, 4, 9, 7, 10, 1, 25, 8, 4, 3, 8, 5, 11, 5, 5, 4, 17, 10, 6, 4, 10, 7, 4, 5, 10, 3, 5, 8, 14, 19, 5, 6, 6, 9, 4, 8, 2, 7, 9, 3, 5, 2, 1, 5, 7, 9, 4, 13, 1, 1, 4, 2, 5, 12, 18, 4, 5, 1, 5, 5, 4, 1, 1, 1, 1, 18, 9, 7, 4, 4, 5, 3, 12, 6, 8, 2, 4, 2, 5, 6, 12, 6, 4, 1, 1, 1, 6, 9, 13, 7, 6, 6, 13, 16, 9, 5, 4, 1, 2, 13, 3, 7, 4, 8, 13, 7, 5, 1, 15, 6, 2, 2, 5, 1, 7, 5, 7, 5, 3, 1, 1, 1, 4, 2, 2, 6, 5, 1, 4, 4, 4, 15, 1, 6, 1, 2, 4, 4, 16, 2, 9, 10, 4, 4, 1, 3, 1, 2, 4, 14, 8, 15, 18, 1, 3, 5, 9, 5, 6, 1, 10, 12, 4, 8, 7, 6, 14, 15, 14, 4, 5, 12, 11, 5, 1, 10, 4, 5, 5, 5, 7, 6, 5, 2, 6, 12, 4, 16, 12, 1, 4, 5, 2, 4, 1, 5, 11, 13, 5, 6, 4, 9, 6, 2, 9, 1, 4, 4, 6, 4, 4, 6, 10, 4, 9, 11, 1, 19, 1, 10, 1, 6, 1, 12, 10, 4, 7, 5, 17, 3, 1, 8, 4, 7, 6, 4, 6, 7, 2, 3, 6, 8, 4, 4, 7, 4, 1, 5, 9, 10, 4, 1, 11, 8, 4, 7, 6, 7, 8, 5, 6, 14, 4, 3, 3, 6, 7, 4, 1, 6, 6, 7, 2, 5, 8, 12, 7, 1, 1, 4, 1, 1, 3, 11, 4, 5, 5, 9, 3, 4, 1, 5, 1, 13, 4, 17, 3, 8, 10, 4, 7, 3, 3, 15, 7, 1, 17, 4, 3, 9, 11, 7, 11, 5, 8, 7, 6, 6, 9, 7, 10, 6, 6, 4, 1, 8, 1, 2, 1, 5, 10, 2, 5, 8, 4, 8, 6, 5, 4, 12, 7, 11, 10, 4, 11, 2, 8, 7, 2, 4, 10, 9, 7, 16, 10, 4, 10, 4, 6, 7, 5, 2, 15, 2, 2, 1, 4, 2, 1, 4, 6, 5, 6, 2, 9, 5, 1, 4, 7, 4, 4, 4, 10, 5, 4, 11, 2, 3, 4, 5, 7, 5, 7, 6, 4, 1, 15, 6, 3, 8, 9, 5, 1, 6, 16, 6, 6, 13, 5, 1, 4, 14, 4, 4, 6, 3, 10, 2, 1, 5, 4, 1, 5, 9, 18, 2, 11, 4, 14, 7, 1, 25, 14, 6, 6, 16, 1, 8, 8, 13, 2, 5, 4, 4, 9, 8, 7, 7, 2, 6, 10, 6, 5, 2, 12, 4, 5, 7, 1, 4, 4, 10, 10, 1, 13, 10, 15, 13, 7, 15, 1, 1, 2, 11, 4, 6, 6, 10, 18, 6, 5, 6, 3, 1, 1, 6, 13, 8, 11, 20, 11, 8, 14, 1, 5, 18, 4, 6, 18, 1, 2, 5, 10, 3, 2, 7, 17, 6, 5, 6, 11, 1, 8, 1, 9, 2, 1, 5, 2, 3, 8, 5, 8, 7, 1, 4, 4, 3, 4, 11, 4, 1, 22, 2, 14, 8, 1, 7, 1, 8, 4, 13, 5, 6, 2, 13, 1, 15, 2, 5, 4, 1, 3, 5, 1, 4, 6, 4, 4, 6, 5, 2, 9, 6, 2, 2, 20, 7, 4, 1, 19, 4, 2, 7, 9, 4, 6, 5, 7, 5, 6, 7, 6, 1, 8, 1, 4, 8, 4, 3, 13, 6, 16, 6, 7, 12, 5, 5, 5, 7, 14, 9, 6, 1, 14, 6, 5, 7, 4, 3, 10, 4, 2, 1, 4, 6, 7, 8, 6, 8, 4, 6, 8, 5, 3, 12, 8, 19, 1, 10, 1, 2, 1, 5, 5, 10, 6, 18, 4, 8, 7, 6, 5, 4, 6, 9, 5, 2, 1, 2, 11, 5, 1, 8, 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diff --git a/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/result.json b/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/result.json
new file mode 100644
index 000000000..fda0441f3
--- /dev/null
+++ b/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/result.json
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diff --git a/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/experiment_state-2024-03-03_07-07-35.json b/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/experiment_state-2024-03-03_07-07-35.json
new file mode 100644
index 000000000..15b5b463f
--- /dev/null
+++ b/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/experiment_state-2024-03-03_07-07-35.json
@@ -0,0 +1,44 @@
+{
+  "trial_data": [
+    [
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+    "_stopper": {
+      "_type": "CLOUDPICKLE_FALLBACK",
+      "value": "8005952c000000000000008c157261792e74756e652e73746f707065722e6e6f6f70948c0b4e6f6f7053746f707065729493942981942e"
+    },
+    "_start_time": 1709474855.2899313,
+    "_last_checkpoint_time": -Infinity,
+    "_session_str": "2024-03-03_07-07-35",
+    "_checkpoint_period": "auto",
+    "_trial_checkpoint_config": {
+      "_type": "CLOUDPICKLE_FALLBACK",
+      "value": "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"
+    },
+    "_resumed": false
+  },
+  "stats": {
+    "start_time": 1709474855.2899313,
+    "timestamp": -Infinity
+  }
+}
\ No newline at end of file

From 00fc89f57e139c75f3299c8456c21b3065d51aeb Mon Sep 17 00:00:00 2001
From: Xiangyu Zhang <xiangyu.zhang@nrel.gov>
Date: Mon, 4 Mar 2024 10:54:05 -0700
Subject: [PATCH 09/17] rllib update: Update the training README file.

---
 languages/python/RLlib/Tensorboard/README.md  |  43 -------------
 languages/python/RLlib/train/README.md        |  51 +++++++++++++--
 ...vents.out.tfevents.1709475088.dummynodeid} | Bin
 .../progress.csv                              |  60 +++++++++---------
 .../result.json                               |  60 +++++++++---------
 .../experiment_state-2024-03-03_07-07-35.json |   2 +-
 .../tutorial_img/running_tensorboard.png      | Bin 0 -> 28322 bytes
 7 files changed, 107 insertions(+), 109 deletions(-)
 delete mode 100644 languages/python/RLlib/Tensorboard/README.md
 rename languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/{events.out.tfevents.1709475088.x1003c5s6b0n0 => events.out.tfevents.1709475088.dummynodeid} (100%)
 create mode 100644 languages/python/RLlib/tutorial_img/running_tensorboard.png

diff --git a/languages/python/RLlib/Tensorboard/README.md b/languages/python/RLlib/Tensorboard/README.md
deleted file mode 100644
index 40e76199c..000000000
--- a/languages/python/RLlib/Tensorboard/README.md
+++ /dev/null
@@ -1,43 +0,0 @@
-# Validating results using Tensorboard
-
-Another way of visualizing the performance of agent training is with [**Tensorboard**](https://www.tensorflow.org/tensorboard). TensorBoard provides visualization and tooling needed for machine learning, deep learning, and reinforcement learning experimentation, for tracking and visualizing metrics such as loss and accuracy. 
-
-Specifically for RL it is useful to visualize metrics such as:
- * Mean, min, and max reward values.
- * Episodes/iteration.
- * Estimated Q-values.
- * Algorithm-specific metrics (e.g. entropy for PPO).
- 
-To visualize results from Tensorboard, first `cd` to the `ray_results` directory:
-```
-cd ~/ray_results/
-```
-Every RL experiment generates a subdirectory named from the OpenAI Gym environment used in the experiment. 
-
-E.g., after running all the examples previously shown in this tutorial, `ray_results` will have a subdirectory named `CartPole-v0`. Within, every experiment using CartPole generates a new subdirectory.
-
-For the purpose of this tutorial, `cd` to the `CartPole-v0` subdirectory and activate one of the environments:
-```
-module purge
-conda activate <your_environment>
-```
-Then, initialize Tensorboard as:
-```
-tensorboard --logdir=. --port 6006
-```
-For a specific training instance, e.g. the `DQN_CartPole-v0_0_2021-04-29_13-49-56gv0j3u93`, do instead:
-```
-tensorboard --logdir=DQN_CartPole-v0_0_2021-04-29_13-49-56gv0j3u93 --port 6006
-```
-If everything works properly, terminal will show:
-```
-Serving TensorBoard on localhost; to expose to the network, use a proxy or pass --bind_all
-TensorBoard 2.5.0 at http://localhost:6006/ (Press CTRL+C to quit)
-```
-Open a new Terminal tab and create a tunnel:
-```
-ssh -NfL 6006:localhost:6006 $USER@el1.hpc.nrel.gov
-```
-Finally, open the above localhost url (`http://localhost:6006/`) in a browser.
-
-The `tune/episode_reward_mean` plot is essentialy the same as the figure plotted from data in the `progress.csv` file. The difference in the x-axis scale has a simple explanation. The `episode_reward_mean` column on the `progress.csv` file shows the reward progress on every training iteration, while the `tune/episode_reward_mean` plot on Tensorboard shows reward progress on every training episode (a single RLlib training iteration consists of thousands of episodes).
\ No newline at end of file
diff --git a/languages/python/RLlib/train/README.md b/languages/python/RLlib/train/README.md
index 1d87bae95..7cc8a4946 100644
--- a/languages/python/RLlib/train/README.md
+++ b/languages/python/RLlib/train/README.md
@@ -2,7 +2,7 @@
 
 This directory contains examples of using RLlib for RL policy training.
 
-### Files in this directory
+### 1. Files in this directory
 
 `train_script.py`: The main python file that sets up and initiates the training process. **It not only works on Kestrel, but can be used for local training if you want to test on your local computer.**
 
@@ -12,9 +12,9 @@ This directory contains examples of using RLlib for RL policy training.
 
 `train_multi_nodes.sh`: The SLURM sbatch script for job submission. This script shows an example of using multiple Kestrel nodes for training.
 
-### Main training script
+### 2. Main training script
 
-Here we walk though the code in `train_script.py` to help the readers understand what each part of the code is doing.
+Here, we walk though the code in `train_script.py` to help the readers understand what each part of the code is doing.
 
 Firstly, loading configuration arguments for this training experiment.
 
@@ -42,7 +42,7 @@ else:
     ray.init(local_mode=args.local_mode)
 ```
 
-Register the Gym environment:
+Register the Gym environment. RLlib prefers a registered Gym environment, but it is just as simple as creating a name that follows typical Gym env name convention (e.g., EnvName-v0, EnvName-v1) and map this to an `env_creator` function:
 ```
 env_name = 'CarPass-v0'
 
@@ -96,7 +96,7 @@ results = tuner.fit()
 ```
 There is an option to set up how model checkpoints are to be saved in the `checkpoint_config`. Within it, `checkpoint_frequency=x` is how frequent the policy model needs to be saved, e.g., every x iterations; `num_to_keep=y` indicates how many best checkpoints to be saved, i.e., the Top y performing policies; and `checkpoint_score_attribute` specifies using which attribute to evaluate the policies (and determine which ones are the "best".)
 
-### SLURM Sbatch scripts
+### 3. SLURM Sbatch scripts
 
 **NOTE: You will need to change the $HPC_HANDLE and $USER variable in the script to be able to run on Kestrel.**
 
@@ -121,3 +121,44 @@ Start RL training by calling the main training script. This is where some hyperp
 ```
 python -u train_script.py --run PPO --redis-password $redis_password --worker-num $worker_num --ip-head $ip_head
 ```
+
+### 4. Viewing learning curves on Tensorboard
+
+Despite the deep learning framework we used is PyTorch, RLlib can help write the training logs so that TensorBoard can be used to visualize the training performance.
+
+As defined in `train_script.py`, the trained policy/model will be saved under the path of `LOG_PATH`, which is the `results` folder in the `train` folder.
+
+After obtaining a computing node on Kestrel, navigate to the train results folder, and you will see different policies from train instances initiated earlier, for example `PPO_2024-03-03_07-07-34` (which is the one we uploaded for an example). Use either of the following commands to start the Tensorboard (with your conda environment activated):
+
+```
+tensorboard --logdir=. --bind_all
+```
+
+or
+
+```
+tensorboard --logdir=PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35 --bind_all
+```
+
+The difference between these two is that the first one will show you the learning curves of all the training instances whereas the latter one specifies one specific instance to show. Once this command is executed, the Tensorboard will start its server to allow you to connect and browse, it's ready when you see the following output in the terminal. 
+
+```
+(rl_hpc)[xzhang2@dummynodeid PPO_2024-03-03_07-07-34]$ tensorboard --logdir=. --bind_all
+TensorFlow installation not found - running with reduced feature set.
+TensorBoard 2.16.2 at http://dummynodeid.hsn.cm.kestrel.hpc.nrel.gov:6006/ (Press CTRL+C to quit)
+```
+
+The `--bind_all` is needed to allow network traffic from your local machine to visit the Tensorboard server.
+
+The URL in the terminal output above does not work directly if you are using local computer's web browser. To make it work, an SSH tunnel needs to be established using the following command from a terminal window on your local machine (assuming you are using Mac, if using Windows machine, please search online on the exact command).
+
+```
+ssh -L 16006:dummynodeid:6006 $USER@kestrel.hpc.nrel.gov
+```
+Remember to replace `dummynodeid` above with your actual computing node's ID. This builds a tunnel between your local computer's 16006 port with the 6006 port on the computing node. Now, when you visit `localhost:16006` on your local machine, you will be able to see the Tensorboard website. Using 16006 here is just in case you have local tensorboard server running on 6006.
+
+<p align="center">
+    <img src="../tutorial_img/running_tensorboard.png" alt="Running Tensorboard" width="70%"><br>
+    <em>You can visit Tensorboard from `localhost:16006` while the server runs on a computing node.</em>
+</p>
+
diff --git a/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/events.out.tfevents.1709475088.x1003c5s6b0n0 b/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/events.out.tfevents.1709475088.dummynodeid
similarity index 100%
rename from languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/events.out.tfevents.1709475088.x1003c5s6b0n0
rename to languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/events.out.tfevents.1709475088.dummynodeid
diff --git a/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/progress.csv b/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/progress.csv
index e4baa57bc..e680b6268 100644
--- a/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/progress.csv
+++ b/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/progress.csv
@@ -1,31 +1,31 @@
 episode_reward_max,episode_reward_min,episode_reward_mean,episode_len_mean,episodes_this_iter,num_faulty_episodes,num_healthy_workers,num_in_flight_async_reqs,num_remote_worker_restarts,num_agent_steps_sampled,num_agent_steps_trained,num_env_steps_sampled,num_env_steps_trained,num_env_steps_sampled_this_iter,num_env_steps_trained_this_iter,num_env_steps_sampled_throughput_per_sec,num_env_steps_trained_throughput_per_sec,timesteps_total,num_steps_trained_this_iter,agent_timesteps_total,done,episodes_total,training_iteration,trial_id,date,timestamp,time_this_iter_s,time_total_s,pid,hostname,node_ip,time_since_restore,iterations_since_restore,info/num_env_steps_sampled,info/num_env_steps_trained,info/num_agent_steps_sampled,info/num_agent_steps_trained,sampler_results/episode_reward_max,sampler_results/episode_reward_min,sampler_results/episode_reward_mean,sampler_results/episode_len_mean,sampler_results/episodes_this_iter,sampler_results/num_faulty_episodes,hist_stats/episode_reward,hist_stats/episode_lengths,sampler_perf/mean_raw_obs_processing_ms,sampler_perf/mean_inference_ms,sampler_perf/mean_action_processing_ms,sampler_perf/mean_env_wait_ms,sampler_perf/mean_env_render_ms,connector_metrics/ObsPreprocessorConnector_ms,connector_metrics/StateBufferConnector_ms,connector_metrics/ViewRequirementAgentConnector_ms,timers/training_iteration_time_ms,timers/sample_time_ms,timers/load_time_ms,timers/load_throughput,timers/learn_time_ms,timers/learn_throughput,timers/synch_weights_time_ms,counters/num_env_steps_sampled,counters/num_env_steps_trained,counters/num_agent_steps_sampled,counters/num_agent_steps_trained,perf/cpu_util_percent,perf/ram_util_percent,sampler_results/hist_stats/episode_reward,sampler_results/hist_stats/episode_lengths,sampler_results/sampler_perf/mean_raw_obs_processing_ms,sampler_results/sampler_perf/mean_inference_ms,sampler_results/sampler_perf/mean_action_processing_ms,sampler_results/sampler_perf/mean_env_wait_ms,sampler_results/sampler_perf/mean_env_render_ms,sampler_results/connector_metrics/ObsPreprocessorConnector_ms,sampler_results/connector_metrics/StateBufferConnector_ms,sampler_results/connector_metrics/ViewRequirementAgentConnector_ms,info/learner/default_policy/num_agent_steps_trained,info/learner/default_policy/num_grad_updates_lifetime,info/learner/default_policy/diff_num_grad_updates_vs_sampler_policy,info/learner/default_policy/learner_stats/allreduce_latency,info/learner/default_policy/learner_stats/grad_gnorm,info/learner/default_policy/learner_stats/cur_kl_coeff,info/learner/default_policy/learner_stats/cur_lr,info/learner/default_policy/learner_stats/total_loss,info/learner/default_policy/learner_stats/policy_loss,info/learner/default_policy/learner_stats/vf_loss,info/learner/default_policy/learner_stats/vf_explained_var,info/learner/default_policy/learner_stats/kl,info/learner/default_policy/learner_stats/entropy,info/learner/default_policy/learner_stats/entropy_coeff
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6, 6, 3, 5, 5, 7, 4, 6, 6, 4, 7, 8, 4, 8, 11, 8, 8, 6, 7, 12, 7, 2, 2, 7, 15, 4, 10, 13, 2, 4, 6, 11, 7, 1, 5, 14, 5, 7, 16, 4, 1, 2, 1, 5, 15, 8, 9, 1, 1, 5, 4, 4, 8, 4, 1, 4, 1, 10, 12, 12, 16, 3, 8, 10, 5, 5, 8, 7, 4, 1, 7, 4, 4, 5, 1, 6, 8, 1, 17, 2, 1, 5, 7, 6, 1, 4, 4, 7, 3, 5, 3, 3, 12, 4, 6, 12, 4, 6, 6, 2, 10, 4, 8, 6, 6, 5, 6, 2, 6, 1, 8, 3, 16, 2, 8, 8, 8, 6, 1, 1, 5, 2, 5, 1, 2, 1, 2, 6, 10, 17, 3, 1, 6, 14, 9, 3, 4, 4, 5, 1, 1, 4, 4, 6, 4, 9, 7, 1, 2, 1, 3, 4, 5, 4, 7, 11, 3, 10, 1, 5, 6, 3, 2, 1, 5, 9, 15, 2, 8, 1, 12, 2, 2, 5, 1, 10, 5, 7, 10, 1, 4, 8, 3, 6, 4, 1, 2, 2, 2, 8, 13, 16, 15, 3, 1, 1, 2, 1, 5, 5, 1, 4, 5, 1, 1, 6, 6, 6, 6, 2, 2, 5, 7, 20, 8, 3, 17, 4, 8, 16, 6, 4, 9, 5, 5, 10, 5, 7, 5, 11, 7, 9, 6, 3, 2, 7, 1, 17, 6, 8, 11, 8, 1, 5, 1, 6, 7, 9, 1, 16, 5, 14, 5, 25, 10, 1, 2, 7, 6, 6, 5, 5, 3, 8, 1, 1, 15, 5, 5, 3, 8, 4, 6, 13, 6, 5, 12, 9, 18, 3, 1, 3, 1, 21, 1, 8, 5, 5, 3, 12, 5, 2, 2, 1, 2, 1, 16, 3, 8, 10, 8, 1, 6, 4, 8, 4, 1, 4, 8, 2, 6, 5, 1, 7, 2, 1, 5, 1, 7, 5, 12, 5, 4, 15, 4, 21, 5, 14, 12, 9, 1, 8, 5, 7, 3, 7, 10, 3, 11, 5, 12, 5, 2, 3, 8, 10, 7, 6, 7, 4, 8, 5, 1, 4, 7, 9, 10, 2, 4, 5, 4, 4, 2, 5, 1, 4, 3, 4, 6, 1, 16, 7, 11, 4, 3, 2, 7, 3, 1, 5, 6, 8, 6, 5, 3, 5, 1, 3, 8, 1, 9, 3, 6, 5, 6, 5, 7, 6, 5, 13, 6, 12, 8, 1, 1, 4, 4, 5, 8, 1, 10, 7, 6, 6, 1, 3, 11, 6, 6, 7, 6, 6, 9, 6, 3, 12, 12, 4, 13, 3, 4, 5, 5, 1, 8, 4, 10, 3, 5, 4, 6, 3, 4, 7, 4, 6, 11, 4, 2, 1, 3, 23, 7, 10, 8, 13, 5, 1, 7, 7, 4, 11, 6, 4, 11, 5, 1, 5, 5, 6, 5, 4, 11, 5, 4, 14, 4, 2, 3, 13, 10, 11, 16, 4, 1, 7, 4, 4, 5, 1, 2, 8, 3, 9, 4, 3, 6, 7, 12, 1, 6, 4, 1, 5, 4, 7, 5, 1, 6, 3, 6, 4, 8, 5, 1, 8, 4, 4, 1, 4, 6, 5, 20, 10, 4, 4, 1, 6, 4, 5, 1, 8, 1, 6, 4, 16, 5, 9, 4, 6, 6, 6, 1, 5, 7, 1, 1, 3, 9, 2, 5, 4, 6, 5, 6, 10, 8, 16, 2, 4, 8, 9, 6, 7, 16, 15, 9, 8, 15, 3, 7, 4, 2, 5, 11, 4, 5, 16, 16, 6, 7, 6, 11, 7, 1, 6, 4, 5, 21, 1, 1, 16, 6, 10, 8, 4, 6, 5, 9, 15, 3, 15, 8, 13, 1, 16, 1, 12, 1, 6, 4, 5, 3, 3, 4, 4, 2, 8, 11, 1, 6, 7, 8, 5, 3, 8, 15, 4, 7, 6, 2, 3, 7, 6, 1, 5, 1, 3, 1, 7, 14, 1, 2, 4, 10, 1, 9, 5, 4, 10, 4, 10, 10, 6, 5, 1, 8, 1, 4, 3, 5, 3, 6, 6, 10, 4, 14, 8, 12, 2, 5, 1, 7, 8, 13, 1, 6, 6, 4, 1, 7, 8, 6, 5, 4, 6, 12, 4, 4, 17, 4, 12, 5, 11, 3, 1, 8, 7, 5, 6, 4, 1, 6, 5, 2, 8, 4, 1, 1, 1, 2, 6, 6, 7, 2, 5, 15, 5, 6, 5, 8, 1, 6, 1, 16, 6, 5, 5, 7, 3, 1, 1, 3, 1, 7, 16, 22, 2, 9, 3, 18, 6, 7, 7, 14, 4, 4, 5, 6, 12, 5, 1, 6, 3, 5, 10, 11, 6, 5, 2, 4, 3, 3, 4, 2, 5, 7, 8, 7, 16, 5, 6, 1, 4, 2, 5, 14, 4, 1, 5, 9, 6, 6, 7, 4, 11, 6, 14, 4, 13, 14, 6, 1, 3, 4, 4, 5, 7, 2, 9, 4, 8, 5, 4, 4, 5, 1, 7, 4, 1, 5, 5, 8, 1, 2, 9, 17, 5, 6, 6, 12, 2, 4, 4, 5, 15, 5, 11, 2, 4, 4, 1, 8, 5, 1, 5, 4, 8, 9, 14, 10, 1, 10, 3, 4, 5, 6, 4, 2, 6, 9, 5, 2, 2, 4, 11, 6, 8, 4, 1, 3, 5, 4, 9, 1, 1, 11, 2, 6, 6, 1, 9, 4, 4, 7, 10, 1, 5, 7, 6, 6, 4, 7, 4, 2, 1, 4, 3, 4, 1, 6, 5, 6, 8, 10, 9, 6, 14, 12, 7, 3, 7, 16, 2, 1, 6, 4, 11, 10, 10, 7, 6, 6, 4, 14, 5, 6, 8, 2, 9, 10, 7, 2, 7, 7, 7, 1, 5, 5, 3, 20, 4, 17, 1, 1, 4, 9, 18, 6, 14, 6, 2, 16, 1, 10, 8, 3, 6, 6, 1, 17, 25, 4, 2, 2, 1, 5, 4, 5, 6, 5, 2, 10, 9, 6, 1, 1, 4, 13, 13, 5, 12, 4, 6, 8, 1, 4, 6, 1, 4, 1, 4, 3, 3, 4, 5, 9, 6, 3, 1, 6, 7, 5, 4, 7, 13, 6, 4, 15, 1, 9, 2, 5, 4, 4, 1, 1, 3, 2, 4, 10, 3, 2, 10, 7, 4, 4, 1, 9, 3, 16, 8, 5, 9, 7, 12, 4, 19, 2, 5, 6, 4, 6, 5, 7, 1, 7, 14, 8, 7, 1, 9, 3, 1, 2, 22, 4, 1, 6, 7, 8, 1, 3, 4, 17, 4, 5, 15, 4, 3, 1, 13, 13, 4, 3, 1, 14, 1, 4, 11, 4, 3, 17, 8, 14, 6, 4, 5, 10, 6, 5, 4, 7, 4, 11, 7, 4, 14, 14, 18, 8, 11, 18, 7, 7, 6, 2, 6, 5, 4, 5, 6, 5, 20, 1, 5, 3, 9, 1, 12, 5, 3, 4, 4, 2, 6, 4, 12, 4, 6, 4, 2, 5, 1, 11, 10, 5, 5, 4, 1, 6, 10, 6, 12, 14, 5, 7, 2, 1, 2, 13, 2, 7, 6, 14, 1, 3, 6, 3, 9, 4, 9, 10, 1, 11, 2, 12, 6, 8, 5, 1, 5, 14, 5, 7, 8, 8, 8, 1, 13, 9, 5, 2, 9, 7, 6, 7, 1, 2, 2, 5, 5, 2, 6, 16, 8, 9, 6, 4, 8, 6, 6, 3, 10, 5, 14, 9, 1, 5, 8, 7, 3, 6, 3, 4, 3, 9, 8, 3, 9, 3, 6, 8, 6, 2, 4, 6, 2, 4, 4, 1, 4, 6, 2, 2, 1, 4, 4, 9, 2, 1, 11, 4, 6, 3, 5, 3, 12, 13, 5, 9, 5, 8, 6, 1, 6, 6, 1, 2, 5, 5, 6, 2, 10, 7, 5, 6, 5, 6, 7, 1, 5, 5, 16, 5, 5, 2, 1, 7, 7, 1, 1, 5, 2, 12, 5, 16, 1, 8, 1, 8, 7, 4, 6, 8, 4, 8, 9, 4, 5, 13, 5, 5, 2, 6, 5, 6, 4, 1, 13, 5, 10, 6, 1, 6, 11, 13, 1, 14, 4, 1, 2, 4, 1, 1, 5, 22, 3, 4, 2, 1, 7, 6, 2, 2, 15, 5, 5, 6, 5, 4, 4, 9, 1, 2, 6, 7, 6, 13, 1, 4, 13, 3, 6, 6, 3, 9, 1, 4, 3, 5, 2, 10, 11, 5, 4, 1, 8, 5, 5, 12, 4, 4, 1, 7, 5, 2, 2, 4, 8, 4, 3, 14, 2, 12, 16, 2, 8, 5, 1, 6, 4, 5, 11, 6, 2, 6, 5, 15, 10, 5, 1, 4, 11, 2, 2, 6, 4, 1, 5, 10, 1, 1, 22, 5, 4, 4, 21, 2, 19, 1, 10, 3, 12, 3, 3, 1, 7, 8, 10, 9, 5, 3, 7, 7, 1, 17, 7, 5, 7, 1, 14, 1, 6, 14, 4, 1, 2, 1, 2, 6, 5, 5, 1, 8, 1, 10, 6, 4, 6, 17, 4, 4, 6, 6, 4, 5, 8, 5, 4, 4, 5, 13, 1, 6, 5, 14, 1, 6, 5, 5, 2, 8, 2, 7, 2, 13, 8, 5, 2, 4, 4, 1, 4, 13, 5, 15, 5, 1, 10, 5, 2, 4, 2, 5, 2, 7, 4, 5, 4, 2, 2, 7, 4, 12, 4, 12, 5, 11, 5, 4, 1, 2, 2, 4, 8, 7, 5, 1, 8, 5, 5, 4, 1, 4, 7, 18, 6, 1, 12, 8, 10, 2, 2, 6, 5, 2, 6, 8, 5, 5, 1, 5, 6, 4, 1, 1, 5, 4, 5, 8, 4, 6, 4, 9, 7, 10, 1, 25, 8, 4, 3, 8, 5, 11, 5, 5, 4, 17, 10, 6, 4, 10, 7, 4, 5, 10, 3, 5, 8, 14, 19, 5, 6, 6, 9, 4, 8, 2, 7, 9, 3, 5, 2, 1, 5, 7, 9, 4, 13, 1, 1, 4, 2, 5, 12, 18, 4, 5, 1, 5, 5, 4, 1, 1, 1, 1, 18, 9, 7, 4, 4, 5, 3, 12, 6, 8, 2, 4, 2, 5, 6, 12, 6, 4, 1, 1, 1, 6, 9, 13, 7, 6, 6, 13, 16, 9, 5, 4, 1, 2, 13, 3, 7, 4, 8, 13, 7, 5, 1, 15, 6, 2, 2, 5, 1, 7, 5, 7, 5, 3, 1, 1, 1, 4, 2, 2, 6, 5, 1, 4, 4, 4, 15, 1, 6, 1, 2, 4, 4, 16, 2, 9, 10, 4, 4, 1, 3, 1, 2, 4, 14, 8, 15, 18, 1, 3, 5, 9, 5, 6, 1, 10, 12, 4, 8, 7, 6, 14, 15, 14, 4, 5, 12, 11, 5, 1, 10, 4, 5, 5, 5, 7, 6, 5, 2, 6, 12, 4, 16, 12, 1, 4, 5, 2, 4, 1, 5, 11, 13, 5, 6, 4, 9, 6, 2, 9, 1, 4, 4, 6, 4, 4, 6, 10, 4, 9, 11, 1, 19, 1, 10, 1, 6, 1, 12, 10, 4, 7, 5, 17, 3, 1, 8, 4, 7, 6, 4, 6, 7, 2, 3, 6, 8, 4, 4, 7, 4, 1, 5, 9, 10, 4, 1, 11, 8, 4, 7, 6, 7, 8, 5, 6, 14, 4, 3, 3, 6, 7, 4, 1, 6, 6, 7, 2, 5, 8, 12, 7, 1, 1, 4, 1, 1, 3, 11, 4, 5, 5, 9, 3, 4, 1, 5, 1, 13, 4, 17, 3, 8, 10, 4, 7, 3, 3, 15, 7, 1, 17, 4, 3, 9, 11, 7, 11, 5, 8, 7, 6, 6, 9, 7, 10, 6, 6, 4, 1, 8, 1, 2, 1, 5, 10, 2, 5, 8, 4, 8, 6, 5, 4, 12, 7, 11, 10, 4, 11, 2, 8, 7, 2, 4, 10, 9, 7, 16, 10, 4, 10, 4, 6, 7, 5, 2, 15, 2, 2, 1, 4, 2, 1, 4, 6, 5, 6, 2, 9, 5, 1, 4, 7, 4, 4, 4, 10, 5, 4, 11, 2, 3, 4, 5, 7, 5, 7, 6, 4, 1, 15, 6, 3, 8, 9, 5, 1, 6, 16, 6, 6, 13, 5, 1, 4, 14, 4, 4, 6, 3, 10, 2, 1, 5, 4, 1, 5, 9, 18, 2, 11, 4, 14, 7, 1, 25, 14, 6, 6, 16, 1, 8, 8, 13, 2, 5, 4, 4, 9, 8, 7, 7, 2, 6, 10, 6, 5, 2, 12, 4, 5, 7, 1, 4, 4, 10, 10, 1, 13, 10, 15, 13, 7, 15, 1, 1, 2, 11, 4, 6, 6, 10, 18, 6, 5, 6, 3, 1, 1, 6, 13, 8, 11, 20, 11, 8, 14, 1, 5, 18, 4, 6, 18, 1, 2, 5, 10, 3, 2, 7, 17, 6, 5, 6, 11, 1, 8, 1, 9, 2, 1, 5, 2, 3, 8, 5, 8, 7, 1, 4, 4, 3, 4, 11, 4, 1, 22, 2, 14, 8, 1, 7, 1, 8, 4, 13, 5, 6, 2, 13, 1, 15, 2, 5, 4, 1, 3, 5, 1, 4, 6, 4, 4, 6, 5, 2, 9, 6, 2, 2, 20, 7, 4, 1, 19, 4, 2, 7, 9, 4, 6, 5, 7, 5, 6, 7, 6, 1, 8, 1, 4, 8, 4, 3, 13, 6, 16, 6, 7, 12, 5, 5, 5, 7, 14, 9, 6, 1, 14, 6, 5, 7, 4, 3, 10, 4, 2, 1, 4, 6, 7, 8, 6, 8, 4, 6, 8, 5, 3, 12, 8, 19, 1, 10, 1, 2, 1, 5, 5, 10, 6, 18, 4, 8, 7, 6, 5, 4, 6, 9, 5, 2, 1, 2, 11, 5, 1, 8, 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4, 1, 10, 4, 6, 5, 8, 1, 10, 7, 4, 2, 17, 5, 3, 16, 1, 10, 1, 1, 4, 2, 14, 12, 5, 1, 7, 9, 6, 1, 19, 5, 1, 5, 10, 5, 10, 2, 9, 3, 3, 1, 6, 5, 1, 6, 8, 5, 4, 1, 4, 4, 6, 1, 22, 17, 11, 15, 1, 10, 7, 2, 13, 1, 13, 6, 8, 1, 5, 7, 8, 14, 21, 5, 14, 5, 13, 6, 5, 6, 5, 5, 4, 2, 7, 2, 4, 14, 4, 1, 9, 7, 6, 5, 14, 5, 1, 4, 13, 5, 12, 4, 6, 5, 1, 1, 4, 2, 1, 10, 13, 10, 4, 5, 8, 1, 2, 1, 15, 1, 4, 4, 16, 1, 5, 5, 7, 4, 6, 14, 5, 1, 4, 5, 1, 2, 5, 6, 7, 3, 4, 4, 3, 6, 1, 6, 8, 2, 6, 4, 5, 5, 5, 12, 5, 1, 11, 5, 3, 5, 9, 1, 4, 5, 4, 4, 3, 5, 3, 21, 7, 5, 2, 2, 6, 4, 4, 6, 6, 10, 3, 6, 6, 5, 3, 23, 1, 3, 25, 13, 6, 7, 3, 5, 7, 6, 1, 1, 19, 5, 5, 14, 1, 4, 1, 15, 3, 2, 6, 1, 4, 5, 5, 6, 1, 8, 8, 2, 2, 6, 4, 2, 5, 6, 3, 1, 5, 5, 6, 8, 8, 2, 3, 4, 4, 9, 8, 21, 1, 7, 2, 3, 13, 3, 11, 5, 10, 1, 5, 6, 5, 2, 7, 6, 2, 1, 1, 7, 8, 16, 9, 5, 17, 16, 6, 5, 16, 24, 3, 6, 5, 8, 1, 20, 3, 8, 1, 1, 8, 4, 5, 9, 8, 2, 7, 4, 4, 2, 4, 2, 5, 7, 5, 4, 24, 1, 2, 2, 5, 7, 12, 1, 10, 13, 1, 3, 11, 16, 12, 5, 1, 6, 6, 9, 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6, 6, 3, 5, 5, 7, 4, 6, 6, 4, 7, 8, 4, 8, 11, 8, 8, 6, 7, 12, 7, 2, 2, 7, 15, 4, 10, 13, 2, 4, 6, 11, 7, 1, 5, 14, 5, 7, 16, 4, 1, 2, 1, 5, 15, 8, 9, 1, 1, 5, 4, 4, 8, 4, 1, 4, 1, 10, 12, 12, 16, 3, 8, 10, 5, 5, 8, 7, 4, 1, 7, 4, 4, 5, 1, 6, 8, 1, 17, 2, 1, 5, 7, 6, 1, 4, 4, 7, 3, 5, 3, 3, 12, 4, 6, 12, 4, 6, 6, 2, 10, 4, 8, 6, 6, 5, 6, 2, 6, 1, 8, 3, 16, 2, 8, 8, 8, 6, 1, 1, 5, 2, 5, 1, 2, 1, 2, 6, 10, 17, 3, 1, 6, 14, 9, 3, 4, 4, 5, 1, 1, 4, 4, 6, 4, 9, 7, 1, 2, 1, 3, 4, 5, 4, 7, 11, 3, 10, 1, 5, 6, 3, 2, 1, 5, 9, 15, 2, 8, 1, 12, 2, 2, 5, 1, 10, 5, 7, 10, 1, 4, 8, 3, 6, 4, 1, 2, 2, 2, 8, 13, 16, 15, 3, 1, 1, 2, 1, 5, 5, 1, 4, 5, 1, 1, 6, 6, 6, 6, 2, 2, 5, 7, 20, 8, 3, 17, 4, 8, 16, 6, 4, 9, 5, 5, 10, 5, 7, 5, 11, 7, 9, 6, 3, 2, 7, 1, 17, 6, 8, 11, 8, 1, 5, 1, 6, 7, 9, 1, 16, 5, 14, 5, 25, 10, 1, 2, 7, 6, 6, 5, 5, 3, 8, 1, 1, 15, 5, 5, 3, 8, 4, 6, 13, 6, 5, 12, 9, 18, 3, 1, 3, 1, 21, 1, 8, 5, 5, 3, 12, 5, 2, 2, 1, 2, 1, 16, 3, 8, 10, 8, 1, 6, 4, 8, 4, 1, 4, 8, 2, 6, 5, 1, 7, 2, 1, 5, 1, 7, 5, 12, 5, 4, 15, 4, 21, 5, 14, 12, 9, 1, 8, 5, 7, 3, 7, 10, 3, 11, 5, 12, 5, 2, 3, 8, 10, 7, 6, 7, 4, 8, 5, 1, 4, 7, 9, 10, 2, 4, 5, 4, 4, 2, 5, 1, 4, 3, 4, 6, 1, 16, 7, 11, 4, 3, 2, 7, 3, 1, 5, 6, 8, 6, 5, 3, 5, 1, 3, 8, 1, 9, 3, 6, 5, 6, 5, 7, 6, 5, 13, 6, 12, 8, 1, 1, 4, 4, 5, 8, 1, 10, 7, 6, 6, 1, 3, 11, 6, 6, 7, 6, 6, 9, 6, 3, 12, 12, 4, 13, 3, 4, 5, 5, 1, 8, 4, 10, 3, 5, 4, 6, 3, 4, 7, 4, 6, 11, 4, 2, 1, 3, 23, 7, 10, 8, 13, 5, 1, 7, 7, 4, 11, 6, 4, 11, 5, 1, 5, 5, 6, 5, 4, 11, 5, 4, 14, 4, 2, 3, 13, 10, 11, 16, 4, 1, 7, 4, 4, 5, 1, 2, 8, 3, 9, 4, 3, 6, 7, 12, 1, 6, 4, 1, 5, 4, 7, 5, 1, 6, 3, 6, 4, 8, 5, 1, 8, 4, 4, 1, 4, 6, 5, 20, 10, 4, 4, 1, 6, 4, 5, 1, 8, 1, 6, 4, 16, 5, 9, 4, 6, 6, 6, 1, 5, 7, 1, 1, 3, 9, 2, 5, 4, 6, 5, 6, 10, 8, 16, 2, 4, 8, 9, 6, 7, 16, 15, 9, 8, 15, 3, 7, 4, 2, 5, 11, 4, 5, 16, 16, 6, 7, 6, 11, 7, 1, 6, 4, 5, 21, 1, 1, 16, 6, 10, 8, 4, 6, 5, 9, 15, 3, 15, 8, 13, 1, 16, 1, 12, 1, 6, 4, 5, 3, 3, 4, 4, 2, 8, 11, 1, 6, 7, 8, 5, 3, 8, 15, 4, 7, 6, 2, 3, 7, 6, 1, 5, 1, 3, 1, 7, 14, 1, 2, 4, 10, 1, 9, 5, 4, 10, 4, 10, 10, 6, 5, 1, 8, 1, 4, 3, 5, 3, 6, 6, 10, 4, 14, 8, 12, 2, 5, 1, 7, 8, 13, 1, 6, 6, 4, 1, 7, 8, 6, 5, 4, 6, 12, 4, 4, 17, 4, 12, 5, 11, 3, 1, 8, 7, 5, 6, 4, 1, 6, 5, 2, 8, 4, 1, 1, 1, 2, 6, 6, 7, 2, 5, 15, 5, 6, 5, 8, 1, 6, 1, 16, 6, 5, 5, 7, 3, 1, 1, 3, 1, 7, 16, 22, 2, 9, 3, 18, 6, 7, 7, 14, 4, 4, 5, 6, 12, 5, 1, 6, 3, 5, 10, 11, 6, 5, 2, 4, 3, 3, 4, 2, 5, 7, 8, 7, 16, 5, 6, 1, 4, 2, 5, 14, 4, 1, 5, 9, 6, 6, 7, 4, 11, 6, 14, 4, 13, 14, 6, 1, 3, 4, 4, 5, 7, 2, 9, 4, 8, 5, 4, 4, 5, 1, 7, 4, 1, 5, 5, 8, 1, 2, 9, 17, 5, 6, 6, 12, 2, 4, 4, 5, 15, 5, 11, 2, 4, 4, 1, 8, 5, 1, 5, 4, 8, 9, 14, 10, 1, 10, 3, 4, 5, 6, 4, 2, 6, 9, 5, 2, 2, 4, 11, 6, 8, 4, 1, 3, 5, 4, 9, 1, 1, 11, 2, 6, 6, 1, 9, 4, 4, 7, 10, 1, 5, 7, 6, 6, 4, 7, 4, 2, 1, 4, 3, 4, 1, 6, 5, 6, 8, 10, 9, 6, 14, 12, 7, 3, 7, 16, 2, 1, 6, 4, 11, 10, 10, 7, 6, 6, 4, 14, 5, 6, 8, 2, 9, 10, 7, 2, 7, 7, 7, 1, 5, 5, 3, 20, 4, 17, 1, 1, 4, 9, 18, 6, 14, 6, 2, 16, 1, 10, 8, 3, 6, 6, 1, 17, 25, 4, 2, 2, 1, 5, 4, 5, 6, 5, 2, 10, 9, 6, 1, 1, 4, 13, 13, 5, 12, 4, 6, 8, 1, 4, 6, 1, 4, 1, 4, 3, 3, 4, 5, 9, 6, 3, 1, 6, 7, 5, 4, 7, 13, 6, 4, 15, 1, 9, 2, 5, 4, 4, 1, 1, 3, 2, 4, 10, 3, 2, 10, 7, 4, 4, 1, 9, 3, 16, 8, 5, 9, 7, 12, 4, 19, 2, 5, 6, 4, 6, 5, 7, 1, 7, 14, 8, 7, 1, 9, 3, 1, 2, 22, 4, 1, 6, 7, 8, 1, 3, 4, 17, 4, 5, 15, 4, 3, 1, 13, 13, 4, 3, 1, 14, 1, 4, 11, 4, 3, 17, 8, 14, 6, 4, 5, 10, 6, 5, 4, 7, 4, 11, 7, 4, 14, 14, 18, 8, 11, 18, 7, 7, 6, 2, 6, 5, 4, 5, 6, 5, 20, 1, 5, 3, 9, 1, 12, 5, 3, 4, 4, 2, 6, 4, 12, 4, 6, 4, 2, 5, 1, 11, 10, 5, 5, 4, 1, 6, 10, 6, 12, 14, 5, 7, 2, 1, 2, 13, 2, 7, 6, 14, 1, 3, 6, 3, 9, 4, 9, 10, 1, 11, 2, 12, 6, 8, 5, 1, 5, 14, 5, 7, 8, 8, 8, 1, 13, 9, 5, 2, 9, 7, 6, 7, 1, 2, 2, 5, 5, 2, 6, 16, 8, 9, 6, 4, 8, 6, 6, 3, 10, 5, 14, 9, 1, 5, 8, 7, 3, 6, 3, 4, 3, 9, 8, 3, 9, 3, 6, 8, 6, 2, 4, 6, 2, 4, 4, 1, 4, 6, 2, 2, 1, 4, 4, 9, 2, 1, 11, 4, 6, 3, 5, 3, 12, 13, 5, 9, 5, 8, 6, 1, 6, 6, 1, 2, 5, 5, 6, 2, 10, 7, 5, 6, 5, 6, 7, 1, 5, 5, 16, 5, 5, 2, 1, 7, 7, 1, 1, 5, 2, 12, 5, 16, 1, 8, 1, 8, 7, 4, 6, 8, 4, 8, 9, 4, 5, 13, 5, 5, 2, 6, 5, 6, 4, 1, 13, 5, 10, 6, 1, 6, 11, 13, 1, 14, 4, 1, 2, 4, 1, 1, 5, 22, 3, 4, 2, 1, 7, 6, 2, 2, 15, 5, 5, 6, 5, 4, 4, 9, 1, 2, 6, 7, 6, 13, 1, 4, 13, 3, 6, 6, 3, 9, 1, 4, 3, 5, 2, 10, 11, 5, 4, 1, 8, 5, 5, 12, 4, 4, 1, 7, 5, 2, 2, 4, 8, 4, 3, 14, 2, 12, 16, 2, 8, 5, 1, 6, 4, 5, 11, 6, 2, 6, 5, 15, 10, 5, 1, 4, 11, 2, 2, 6, 4, 1, 5, 10, 1, 1, 22, 5, 4, 4, 21, 2, 19, 1, 10, 3, 12, 3, 3, 1, 7, 8, 10, 9, 5, 3, 7, 7, 1, 17, 7, 5, 7, 1, 14, 1, 6, 14, 4, 1, 2, 1, 2, 6, 5, 5, 1, 8, 1, 10, 6, 4, 6, 17, 4, 4, 6, 6, 4, 5, 8, 5, 4, 4, 5, 13, 1, 6, 5, 14, 1, 6, 5, 5, 2, 8, 2, 7, 2, 13, 8, 5, 2, 4, 4, 1, 4, 13, 5, 15, 5, 1, 10, 5, 2, 4, 2, 5, 2, 7, 4, 5, 4, 2, 2, 7, 4, 12, 4, 12, 5, 11, 5, 4, 1, 2, 2, 4, 8, 7, 5, 1, 8, 5, 5, 4, 1, 4, 7, 18, 6, 1, 12, 8, 10, 2, 2, 6, 5, 2, 6, 8, 5, 5, 1, 5, 6, 4, 1, 1, 5, 4, 5, 8, 4, 6, 4, 9, 7, 10, 1, 25, 8, 4, 3, 8, 5, 11, 5, 5, 4, 17, 10, 6, 4, 10, 7, 4, 5, 10, 3, 5, 8, 14, 19, 5, 6, 6, 9, 4, 8, 2, 7, 9, 3, 5, 2, 1, 5, 7, 9, 4, 13, 1, 1, 4, 2, 5, 12, 18, 4, 5, 1, 5, 5, 4, 1, 1, 1, 1, 18, 9, 7, 4, 4, 5, 3, 12, 6, 8, 2, 4, 2, 5, 6, 12, 6, 4, 1, 1, 1, 6, 9, 13, 7, 6, 6, 13, 16, 9, 5, 4, 1, 2, 13, 3, 7, 4, 8, 13, 7, 5, 1, 15, 6, 2, 2, 5, 1, 7, 5, 7, 5, 3, 1, 1, 1, 4, 2, 2, 6, 5, 1, 4, 4, 4, 15, 1, 6, 1, 2, 4, 4, 16, 2, 9, 10, 4, 4, 1, 3, 1, 2, 4, 14, 8, 15, 18, 1, 3, 5, 9, 5, 6, 1, 10, 12, 4, 8, 7, 6, 14, 15, 14, 4, 5, 12, 11, 5, 1, 10, 4, 5, 5, 5, 7, 6, 5, 2, 6, 12, 4, 16, 12, 1, 4, 5, 2, 4, 1, 5, 11, 13, 5, 6, 4, 9, 6, 2, 9, 1, 4, 4, 6, 4, 4, 6, 10, 4, 9, 11, 1, 19, 1, 10, 1, 6, 1, 12, 10, 4, 7, 5, 17, 3, 1, 8, 4, 7, 6, 4, 6, 7, 2, 3, 6, 8, 4, 4, 7, 4, 1, 5, 9, 10, 4, 1, 11, 8, 4, 7, 6, 7, 8, 5, 6, 14, 4, 3, 3, 6, 7, 4, 1, 6, 6, 7, 2, 5, 8, 12, 7, 1, 1, 4, 1, 1, 3, 11, 4, 5, 5, 9, 3, 4, 1, 5, 1, 13, 4, 17, 3, 8, 10, 4, 7, 3, 3, 15, 7, 1, 17, 4, 3, 9, 11, 7, 11, 5, 8, 7, 6, 6, 9, 7, 10, 6, 6, 4, 1, 8, 1, 2, 1, 5, 10, 2, 5, 8, 4, 8, 6, 5, 4, 12, 7, 11, 10, 4, 11, 2, 8, 7, 2, 4, 10, 9, 7, 16, 10, 4, 10, 4, 6, 7, 5, 2, 15, 2, 2, 1, 4, 2, 1, 4, 6, 5, 6, 2, 9, 5, 1, 4, 7, 4, 4, 4, 10, 5, 4, 11, 2, 3, 4, 5, 7, 5, 7, 6, 4, 1, 15, 6, 3, 8, 9, 5, 1, 6, 16, 6, 6, 13, 5, 1, 4, 14, 4, 4, 6, 3, 10, 2, 1, 5, 4, 1, 5, 9, 18, 2, 11, 4, 14, 7, 1, 25, 14, 6, 6, 16, 1, 8, 8, 13, 2, 5, 4, 4, 9, 8, 7, 7, 2, 6, 10, 6, 5, 2, 12, 4, 5, 7, 1, 4, 4, 10, 10, 1, 13, 10, 15, 13, 7, 15, 1, 1, 2, 11, 4, 6, 6, 10, 18, 6, 5, 6, 3, 1, 1, 6, 13, 8, 11, 20, 11, 8, 14, 1, 5, 18, 4, 6, 18, 1, 2, 5, 10, 3, 2, 7, 17, 6, 5, 6, 11, 1, 8, 1, 9, 2, 1, 5, 2, 3, 8, 5, 8, 7, 1, 4, 4, 3, 4, 11, 4, 1, 22, 2, 14, 8, 1, 7, 1, 8, 4, 13, 5, 6, 2, 13, 1, 15, 2, 5, 4, 1, 3, 5, 1, 4, 6, 4, 4, 6, 5, 2, 9, 6, 2, 2, 20, 7, 4, 1, 19, 4, 2, 7, 9, 4, 6, 5, 7, 5, 6, 7, 6, 1, 8, 1, 4, 8, 4, 3, 13, 6, 16, 6, 7, 12, 5, 5, 5, 7, 14, 9, 6, 1, 14, 6, 5, 7, 4, 3, 10, 4, 2, 1, 4, 6, 7, 8, 6, 8, 4, 6, 8, 5, 3, 12, 8, 19, 1, 10, 1, 2, 1, 5, 5, 10, 6, 18, 4, 8, 7, 6, 5, 4, 6, 9, 5, 2, 1, 2, 11, 5, 1, 8, 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diff --git a/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/result.json b/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/result.json
index fda0441f3..d48be14b5 100644
--- a/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/result.json
+++ b/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/PPO_CarPass-v0_623f2_00000_0_2024-03-03_07-07-35/result.json
@@ -1,30 +1,30 @@
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diff --git a/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/experiment_state-2024-03-03_07-07-35.json b/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/experiment_state-2024-03-03_07-07-35.json
index 15b5b463f..a18ae0902 100644
--- a/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/experiment_state-2024-03-03_07-07-35.json
+++ b/languages/python/RLlib/train/results/PPO_2024-03-03_07-07-34/experiment_state-2024-03-03_07-07-35.json
@@ -2,7 +2,7 @@
   "trial_data": [
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From 4590fa6db17d569016618a4b7f1c7ab55fb57c86 Mon Sep 17 00:00:00 2001
From: zxymark221 <zxymark@vt.edu>
Date: Wed, 6 Mar 2024 13:21:43 -0700
Subject: [PATCH 10/17] rllib update: add custom environment readme.

---
 .../python/RLlib/custom_gym_env/README.md     | 134 ++++++++++--------
 .../python/RLlib/custom_gym_env/custom_env.py |  14 +-
 .../custom_gym_env/custom_env_trainer.py      |  53 -------
 .../tutorial_img/custom_env_illustration.png  | Bin 0 -> 80825 bytes
 .../RLlib/tutorial_img/gym_and_agent.png      | Bin 0 -> 66888 bytes
 5 files changed, 77 insertions(+), 124 deletions(-)
 delete mode 100644 languages/python/RLlib/custom_gym_env/custom_env_trainer.py
 create mode 100644 languages/python/RLlib/tutorial_img/custom_env_illustration.png
 create mode 100644 languages/python/RLlib/tutorial_img/gym_and_agent.png

diff --git a/languages/python/RLlib/custom_gym_env/README.md b/languages/python/RLlib/custom_gym_env/README.md
index 9efe10b04..6fb545f5d 100644
--- a/languages/python/RLlib/custom_gym_env/README.md
+++ b/languages/python/RLlib/custom_gym_env/README.md
@@ -1,84 +1,92 @@
-# Create Gym environments from scratch
+# Create a customized Gym environment
 
-So far, only benchmark Gym environments were used in order to demonstrate the processes for running experiments. It is time now to see how one can create their own Gym environment, carefully tailor-made to one's needs. OpenAI Gym functionality allows the creation of custom-made environments using the same structure as the benchmark ones. 
+This section demonstrate how one can create their own Gym environment, carefully tailor-made to one's needs. At NREL, this could be for an optimal control problem in grid operation, building energy management or traffic control.
 
-Custom-made environments can become extremely complex due to the mechanics involved and may require many subscripts that perform parts of the simulation. Nevertheless, the basis of all environments is simply a Python class that inherits the `gym.Env` class, where the user can implement the three main Gym functions and define any hyperapameters necessary:
- * `def __init__(self)`: Initializes the environment. It defines initial values for variables/hyperparameters and may contain other necessary information. It also defines the dimensionality of the problem. Dimensionality is expressed at the sizes of the observation and action spaces, which are given using the parameters `self.observation_space` and `self.action_space`, respectively. Depending on their nature, they can take discrete, continuous, or a combination of values. OpenAI provides [detailed examples](https://gym.openai.com/docs/) of each one of these types of spaces.
- * `def reset(self)`: When called, it *resets* the environment on a previous state (hence the name). This state can either be a user-defined initial state or it may be a random initial position. The latter can be found on environments that describe locomotion like `CartPole`, where the initial state can be any possible position of the pole on the cart.
- * `def step(self, action)`: The heart of the class. It defines the inner mechanics of the environment, hence it can be seen as some kind of simulator. Its main input is the sampled action, which when acted upon moves the environment into a new state and calculates the new reward. The new state and reward are two of the function's output and they are necessary for policy training since they are also inputs to the policy network. Other outputs include a boolean variable `done` that is **True** when the environment reaches its final state (if it exists), and **False** otherwise<sup>*</sup>, as well as a dictionary (`info`) with user-defined key-value objects that contain further information from the inner workings of the environment. 
- 
-<sup>*</sup>*Many environments do not consider a final state, since it might not make sense (e.g. a traffic simulator for fleets of autonomous ridesharing vehicles that reposition themselves based on a certain criterion. In this case the reward will get better every time, but there is no notion of a final vehicle position).*
+## High-level overview
 
-Directions of how to create and register a custom-made OpenAI Gym environment are given below.
+To facilitate the deep RL implementation and tests of new algorithms, OpenAI Gym has been the standard interface to connect RL agent/algorithms with the problems. Given such a standard interface, RL training and experiments can be done in a plug-and-play manner, as shown in the figure below. We can use any RL agent implementation (e.g., RLlib and Stable-Baseline3) with different RL algorithms (e.g., PPO, SAC, A3C, and DDPG) to learn a policy for different problems (e.g., cart-pole, lunar landing or the problem of your interest) via the standard interface.
 
-## Create an environment class
+<p align="center">
+    <img src="../tutorial_img/gym_and_agent.png" alt="Running Tensorboard" width="70%"><br>
+    <em>The interaction between Gym environment and RL agent.</em>
+</p>
 
-As stated above, the basis of any Gym environment is a Python class that inherits the `gym.Env` class. After importing the gym package, define the class as:
-```python
-import gym
+To allow using RL training framework such as RLlib in this tutorial to train an optimal policy for the problem of our interest, the customized environment should follow the Gym API standard.
 
-class BasicEnv(gym.Env):(...)
-```
-The example environment is very simple and is represented by two possible states (0, 1) and 5 possible actions (0-4). For the purposes of this tutorial, consider state 0 as the initial state, and state 1 as the final state.
+## Gym environment API structure 
+
+After the latest release of 0.26.2, in the [OpenAI Gym repo](https://github.com/openai/gym), it is [announced](https://github.com/openai/gym?tab=readme-ov-file#important-notice) that all future maintenance and updates are moved to [Gymnasium](https://github.com/Farama-Foundation/Gymnasium). So this tutorial will follow the Gymnasium API guidelines, but we will still refer the environment as "gym" environment for simplicity.
+
+This tutorial also focuses on __episodic__ environment, meaning the optimal control is implemented on a limited step control horizon. The episode ends either when a fixed number of steps are reached or terminal states are reached. 
 
-Define the dimensions of observation and action spaces in the `def __init__(self)` function:
-```python
-def __init__(self):
-    self.action_space = gym.spaces.Discrete(5) # --> Actions take values in the 0-4 interval
-    self.observation_space = gym.spaces.Discrete(2) # --> Two possible states [0,1]
+As the first step, import gymnasium
 ```
-Both spaces take discrete values, therefore they are defined using Gym's `Discrete` function. Other possible functions are `Box` for continuous single- or multi-dimensional observations and states, `MultiDiscrete` for vectors of discrete values, etc. OpenAi provides [detailed explanation](https://gym.openai.com/docs/) for all different space forms.
+import gymnasium as gym
+```
+
+The cusmtom-made gym environment should follow the following structure with three core functions:
 
-Next, define the `def reset(self)` function:
-```python
-def reset(self):
-    state = 0
-    return state
 ```
-In this example, the reset function simply returns the environment to the initial state.
+class CustomEnv(gym.Env):
+
+    def __init__(self):
+        # Initialized the environment.
+        # Called only once when instantiate this class.
+        ...
+    
+    def reset(self, seed=None, options={}):
+        # Reset the environment to the beginning of the control episode.
+        # Called once an episode is complete.
+        ...
+        return obs, info
+    
+    def step(self, action):
+        # Implement the control using the provided action.
+        # Called at each step/control interval within the episode.
+        ...
+        return obs, reward, terminated, truncated, info
+```
 
-Finally, define the `def step(self, action)` function, which takes as input the sampled action. Here the step function takes the environment at state 1 and based on the action, returns a reward of 1 or -1:
-```python
-def step(self, action):
-    state = 1
+Next, we explain more details:
 
-    if action == 2:
-        reward = 1
-    else:
-        reward = -1
+Typically, the environment should inherit the `gym.Env` class by definining `class CustomEnv(gym.Env)`. The three core gym functions are:
 
-    done = True
-    info = {}
+ * `def __init__(self)`: Initializes the environment. More specifically, it generally involves the following three tasks:
+   - Defining necessary variables/hyperparameters.  
+   - Defining the dimensionality of the observation and action space for the problem, which are given using the parameters `self.observation_space` and `self.action_space`, respectively. Depending on their nature, they can take discrete, continuous, or a combination of values. See [this link](https://gymnasium.farama.org/api/spaces/) for more details.
+   - [Optional] Initializing the external simulator if you need one.
+ * `def reset(self, seed=None, options={})`: RL training requires repeated trial-and-error. Once an episode ends, either terminated or truncated, the `reset` function allows the agent to reset the environment back to the initial state, start over  and try again. 
+ Specific things could include:
+   - Resetting the state of the environment
+   - Resetting other utilities, such as setting the step counter back to zero, i.e., `self.step_count = 0`.
+   - Resetting the simulator if you have one.
 
-    return state, reward, done, info
-```
-That's it, the new Gym environment is ready! Make note that there is one more function usually found on Gym environments. This is the `def render(self)` function, and is called in random intervals throughout training returning a "snapshot" of the environment at that time. While this is helpful for evaluating the agent training process, it is not necessary for the actual training process. OpenAI documentation [provides](https://gym.openai.com/docs/#environments) details for every one of these functions.
+   Argument `seed` is used to set the random seed and `options` can pass in some desired configuration for resetting, i.e., resetting to a specific inital state if otherwise generated randomly.
 
-You can find the [full script](https://github.com/erskordi/HPC/blob/HPC-RL/languages/python/openai_rllib/custom_gym_env/custom_env.py) of this environment in the repo.
+   Outputs of the reset function are `obs` and `info`, `obs` is the observation that the RL agent uses as a policy input, while `info` can be a dictionary including some auxilary information (not necessary for learning, but could be used for other purposes such as debugging.)
 
-## Run experiments on RLlib
-Let's now train the agent with RLlib. The [full trainer script](https://github.com/erskordi/HPC/blob/HPC-RL/languages/python/openai_rllib/custom_gym_env/custom_env_trainer.py) is given at the repo.
+ * `def step(self, action)`: It defines the inner mechanics of the environment, and moves the simulation one step ahead using the provided `action`. Specific tasks inside this function can be categorized into the following four:
+    - Advance the system for one step through the state transition function $s_{t+1} = f(s_t, a_t)$ defined by the Markov decision process. If you have external simulator, take one step in the simulator as well. Obtain the new observation and remember to increase to the step counter if necessary: `self.step_count += 1`.
+    - Calculate the reward based on this step's control.
+    - Determine if current episode should end or not, either terminated or truncated. For example, if `self.step_count >= MAX_STEP_ALLOWED`, set `truncated=True`.
+    - [Optional] Prepare additional information and put them in the `info` dictionary. 
 
-The trainer is almost identical to [the one used before](https://github.com/erskordi/HPC/blob/HPC-RL/languages/python/openai_rllib/simple-example/simple_trainer.py), with few additions that are necessary to register the new environment.
+    Outputs of this function are:
+    - `obs`: Usually a Numpy array collecting the new observation after the control of this step.
+    - `reward`: A scalar reward reflecting the performance of this step's control.
+    - `terminated`: A Boolean reflecting if terminal states are reached. E.g., the goal area is reached.
+    - `truncated`: A Boolean reflecting if time limit is reached or other conditions that require stops the simulation.
+    - `info`: A python dictionary containing auxilary information. If none, use `info={}`.
 
-At first, along with `ray` and `tune`, import:
-```python
-from ray.tune.registry import register_env
-from custom_env import BasicEnv
-```
-The `register_env` function is used to register the new environment, which is imported from the `custom_env.py`.
-
-Function `register_env` takes two arguments:
-* Training name of the environment, chosen by the developer.
-* Actual name of the environment (`BasicEnv`) in a `lambda config:` function.
-```python
-env_name = "custom-env"
-register_env(env_name, lambda config: BasicEnv())
-```
-Once again, RLlib provides [detailed explanation](https://docs.ray.io/en/master/rllib-env.html) of how `register_env` works.
+## Example: create a customized environment
+
+This tutorial provides an example of customized environment called "CarPass", letting an RL agent to learn how to control a moving car to the target way point as fast as possible while avoid colliding with a parked stationary car and be out of bound. The figure below shows an illustration.
 
-The `tune.run` function, instead of `args.name_env`, it uses the `env_name` defined above.
+<p align="center">
+    <img src="../tutorial_img/custom_env_illustration.png" alt="Running Tensorboard" width="40%"><br>
+    <em>Illustration of the car pass environment.</em>
+</p>
 
-That's all! Proceed with agent training using any of the slurm scripts provided by the repo.
+See the [full implementation](custom_env.py) for details, and the comments and docstrings should provide some explanations. 
 
-As a final note, creating custom-made OpenAI Gym environment is more like an art than science. The main issue is to really clarify what the environment represents and how it works, and then define this functionality in Python.
\ No newline at end of file
+One thing to notice is that there is a `render()` function in the customized environment, which is used to render the 2D image as shown above for visualization purpose. It is optional, so no need to implement `render()` if not necessary.
diff --git a/languages/python/RLlib/custom_gym_env/custom_env.py b/languages/python/RLlib/custom_gym_env/custom_env.py
index 5b59c7c1c..923f0257b 100644
--- a/languages/python/RLlib/custom_gym_env/custom_env.py
+++ b/languages/python/RLlib/custom_gym_env/custom_env.py
@@ -20,10 +20,9 @@ class CarPassEnv(gym.Env):
         and eventually reach to a point directly in front of the stationary
         car. No physics such as inertia is considered (but can be easily
         added). A car is represented using a circle, the moving car has radius 
-        of 1.5 and the stationary car has radius between 0.8 and 1.8 (the size 
-        of the car changes). Two car collide into each other when the circle 
-        overlaps. The coordinate of the destination is fixed at (2, 5) while 
-        the center of the stationary car is at (2, 0).
+        of 1.5 and the stationary car has radius of 1.0. Two car collide into 
+        each other when the circle overlaps. The coordinate of the destination
+        is fixed at (2, 5) while the center of the stationary car is at (2, 0).
 
         Observation:
         Type: Box(2)
@@ -39,7 +38,7 @@ class CarPassEnv(gym.Env):
 
         Rewards:
         Type                  Value       Description
-        Time lapse            -1          Represent the elapse of time, so the 
+        Time lapse            -10         Represent the elapse of time, so the 
                                           task will be finished asap.
         Collision/OOB         -100        Two cars collide or the moving car is
                                           out of boundary, both end the game.
@@ -203,7 +202,6 @@ def reset(self, seed=None, options={}):
             options = {}
 
         self.moving_car_position = np.array([2.0, -12.0])
-        # Choose the size of the car parked ahead.
         self.stationary_car_size = 1.0
         self.step_count = 0
 
@@ -326,14 +324,14 @@ def close(self):
     episodic_reward = 0.0
 
     while not done:
-        # env.render()
+        env.render()
         act = env.action_space.sample()
         obs, reward, terminated, truncated, info = env.step(act)
         print(obs)
         done = (terminated or truncated)
         episodic_reward += reward
     
-    # env.render()
+    env.render()
 
     print("Reward this episode is %f" % episodic_reward)
     print("Steps this episode is %d" % env.step_count)
diff --git a/languages/python/RLlib/custom_gym_env/custom_env_trainer.py b/languages/python/RLlib/custom_gym_env/custom_env_trainer.py
deleted file mode 100644
index bb3ed8ce9..000000000
--- a/languages/python/RLlib/custom_gym_env/custom_env_trainer.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import argparse
-import os
-
-import ray
-from ray import tune
-from ray.tune.registry import register_env
-from custom_env import BasicEnv
-
-###############################################
-## Command line args
-###############################################
-parser = argparse.ArgumentParser(description="Script for training RLLIB agents")
-parser.add_argument("--num-cpus", type=int, default=0)
-parser.add_argument("--num-gpus", type=int, default=0)
-parser.add_argument("--run", type=str, default="PPO")
-parser.add_argument("--redis-password", type=str, default=None)
-parser.add_argument("--local-mode", action="store_true")
-args = parser.parse_args()
-print(args)
-
-################################################
-## RLLIB SETUP
-################################################
-if args.redis_password is None:
-    # Single node
-    ray.init(local_mode=args.local_mode)
-    num_cpus = args.num_cpus - 1
-else:
-    # On a cluster
-    ray.init(_redis_password=args.redis_password, address=os.environ["ip_head"])
-    num_cpus = args.num_cpus - 1
-
-################################################
-## Register custom environment
-################################################
-env_name = "custom-env"
-register_env(env_name, lambda config: BasicEnv())
-
-######################################
-## Run TUNE Experiments!
-######################################
-tune.run(
-    args.run,
-    name=env_name,
-    local_dir="/scratch/$USER/ray_results",
-    stop={"training_iteration": 100},
-    config={
-        "env": env_name,
-        "num_workers": num_cpus, 
-        "num_gpus": args.num_gpus,
-        "ignore_worker_failures": True
-        }
-    )
diff --git a/languages/python/RLlib/tutorial_img/custom_env_illustration.png b/languages/python/RLlib/tutorial_img/custom_env_illustration.png
new file mode 100644
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HcmV?d00001


From 2f602b1efe3eef8529f9501714bae98fa7e30bd3 Mon Sep 17 00:00:00 2001
From: zxymark221 <zxymark@vt.edu>
Date: Wed, 6 Mar 2024 13:25:39 -0700
Subject: [PATCH 11/17] rllib update: remove old examples.

---
 .../python/RLlib/simple-example-gpu/README.md |  186 ---
 .../simple-example-gpu/env_example_gpu.yml    |   20 -
 .../env_example_optimized_tf.yml              |   15 -
 .../RLlib/simple-example-gpu/gpu_trainer.sh   |   77 -
 .../gpu_trainer_optimized_tf.sh               |   82 -
 .../simple-example-gpu/simple_trainer.py      |   47 -
 .../python/RLlib/simple-example/README.md     |  413 -----
 .../images/ppo_rew_comparison.png             |  Bin 542518 -> 0 bytes
 .../simple-example/multi_node_trainer.sh      |   59 -
 .../RLlib/simple-example/simple_trainer.py    |   46 -
 .../RLlib/simple-example/slurm-6800229.out    | 1326 -----------------
 11 files changed, 2271 deletions(-)
 delete mode 100644 languages/python/RLlib/simple-example-gpu/README.md
 delete mode 100644 languages/python/RLlib/simple-example-gpu/env_example_gpu.yml
 delete mode 100644 languages/python/RLlib/simple-example-gpu/env_example_optimized_tf.yml
 delete mode 100644 languages/python/RLlib/simple-example-gpu/gpu_trainer.sh
 delete mode 100644 languages/python/RLlib/simple-example-gpu/gpu_trainer_optimized_tf.sh
 delete mode 100644 languages/python/RLlib/simple-example-gpu/simple_trainer.py
 delete mode 100644 languages/python/RLlib/simple-example/README.md
 delete mode 100644 languages/python/RLlib/simple-example/images/ppo_rew_comparison.png
 delete mode 100644 languages/python/RLlib/simple-example/multi_node_trainer.sh
 delete mode 100644 languages/python/RLlib/simple-example/simple_trainer.py
 delete mode 100644 languages/python/RLlib/simple-example/slurm-6800229.out

diff --git a/languages/python/RLlib/simple-example-gpu/README.md b/languages/python/RLlib/simple-example-gpu/README.md
deleted file mode 100644
index 9f146390e..000000000
--- a/languages/python/RLlib/simple-example-gpu/README.md
+++ /dev/null
@@ -1,186 +0,0 @@
-# Experimenting using GPUs
-
-It is now time to learn running experiments using GPU nodes on Eagle that can boost training times considerably. GPU nodes however is better to be utilized only in cases of environments with very large observation and/or action spaces. CartPole will be used again for establishing a template.
-
-## Creating Anaconda environment
-
-First thing to do is to create a new environment, this time installing `Tensorflow-GPU`. This is the specialized Tensorflow distribution that is able to recognize and utilize GPU hardware in a computer system. For convenience, the repo provides a validated sample [yaml file](https://github.com/erskordi/HPC/blob/HPC-RL/languages/python/openai_rllib/simple-example-gpu/env_example_gpu.yml) that is tuned to create an Anaconda environment on Eagle with Tensorflow-GPU in it. For installing the new environment, follow the same process as before:
-```
-conda env create --prefix=/<path_to_chosen_directory>/env_example_gpu -f env_example_gpu.yml 
-```
-
-### **Only for Eagle users:** Creating Anaconda environment using Optimized Tensorflow
-
-NREL's HPC group has recently created [a set of optimized Tensorflow drivers](https://github.com/NREL/HPC/tree/master/general/Optimized_TF) that maximize the efficiency of utilizing Eagle's Tesla V100 GPU units. The drivers are created for various Python 3 and Tensorflow 2.x.x versions. 
-
-The repo provides an [Anaconda environment version](https://github.com/erskordi/HPC/blob/HPC-RL/languages/python/openai_rllib/simple-example-gpu/env_example_optimized_tf.yml) for using these drivers. This environment is based on one of the [example environments](https://github.com/NREL/HPC/blob/master/general/Optimized_TF/py37tf22.yml) provided in the [Optimized TF repo](https://github.com/NREL/HPC/tree/master/general/Optimized_TF).
-
-**The provided Anaconda environment currently works for Python 3.7, Tensorflow 2.2, and the latest Ray version**
-
-*Make sure to follow the [instructions for installing this particular environment](https://github.com/NREL/HPC/tree/master/general/Optimized_TF) explicitly!*
-
-## Allocate GPU node 
-
-*The following instructions are the same for both regular and Optimized TF versions of the Anaconda environments*
-
-Running experiments with combined CPU and GPU nodes is not so straightforward as running them using only CPU nodes (either single or multiple nodes). Particularly, heterogenous jobs using slurm have to be submitted.
-
-Begin at first by specifying some basic options, similarly to previous section:
-```batch
-#!/bin/bash  --login
-
-#SBATCH --account=A<account>
-#SBATCH --job-name=cartpole-gpus
-#SBATCH --time=00:10:00
-```
-The slurm script will clearly define the various jobs. These jobs include the CPU nodes that will carry the environment rollouts, and the GPU node for policy learning. Eagle has 44 GPU nodes and each node has 2 GPUs. Either request one GPU per node (`--gres=gpu:1`), or both of them (`--gres=gpu:2`). For the purposes of this tutorial, one GPU core on a single node is utilized.
-
-In total, slurm nodes can be categorized as: 
- * A head node, and multiple rollout nodes (as before)
- * A policy training node (GPU)
-
-Include the `hetjob` header for both the rollout nodes and the policy training node. Three CPU nodes are requested to be used for rollouts and a single GPU node is requested for policy learning:
-```batch
-# Ray head node
-#SBATCH --nodes=1
-#SBATCH --tasks-per-node=1
-
-# Rollout nodes - Nodes with multiple runs of OpenAI Gym 
-#SBATCH hetjob
-#SBATCH --nodes=3
-#SBATCH --tasks-per-node=1
-#SBATCH --cpus-per-task=36
-
-# Policy training node - This is the GPU node
-#SBATCH hetjob
-#SBATCH --nodes=1
-#SBATCH --tasks-per-node=1
-#SBATCH --partition=debug
-#SBATCH --gres=gpu:1
-```
-Of course, any number of CPU/GPU nodes can be requested, depending on problem complexity. 
-
-As an example, a single node and perhaps just a single CPU core may be requested. Now, it is more reasonable to request GPUs for an OpenAI Gym environment that utilizes high-dimensional observation and/or action spaces. Hence, the first priority would be to start with multiple CPU nodes, and request GPUs only if they are needed.
-
-For the three types of nodes (head, rollouts, training), define three separate groups:
-```batch
-head_node=$(scontrol show hostnames $SLURM_JOB_NODELIST_HET_GROUP_0)
-rollout_nodes=$(scontrol show hostnames $SLURM_JOB_NODELIST_HET_GROUP_1)
-rollout_nodes_array=( $rollout_nodes )
-learner_node=$(scontrol show hostnames $SLURM_JOB_NODELIST_HET_GROUP_2)
-echo "head node    : "$head_node
-echo "rollout nodes: "$rollout_nodes
-echo "learner node : "$learner_node
-```
-Each group of nodes requires its separate `srun` command so that they will run independently of each other.
-```batch
-echo "starting head node at $head_node"
-srun --pack-group=0 --nodes=1 --ntasks=1 -w $head_node ray start --block --head \
---node-ip-address="$ip_prefix" --port=$port --redis-password=$redis_password & # Starting the head
-sleep 10
-
-echo "starting rollout workers"
-for ((  i=0; i<$rollout_node_num; i++ ))
-do
-  rollout_node=${rollout_nodes_array[$i]}
-  echo "i=${i}, rollout_node=${rollout_node}"
-  srun --pack-group=1 --nodes=1 --ntasks=1 -w $rollout_node \
-   ray start --block --address "$ip_head" --redis-password=$redis_password & # Starting the workers
-  sleep 5
-done
-
-echo "starting learning on GPU"
-srun --pack-group=2 --nodes=1 --gres=gpu:1 -w $learner_node ray start --block --address "$ip_head" --redis-password=$redis_password &
-```
-The slurm commands for the head and rollout nodes are identical to those from the previous section. A third command is also added for engaging the GPU node.
-
-Finally, call
-```batch
-python -u simple_trainer.py --redis-password $redis_password --num-cpus $rollout_num_cpus --num-gpus 1
-```
-to begin training. Add the `---num-gpus` argument to include the requested GPU node (or nodes in case of `--gres=gpu:2`) for policy training. There is no need to manually declare the GPU for policy training in the `simple_trainer.py`, RLlib will automatically recognize the available GPU and use it accordingly.
-
-The repo contains the complete slurm file versions for both [`env_example_gpu`](https://github.com/erskordi/HPC/blob/HPC-RL/languages/python/openai_rllib/simple-example-gpu/gpu_trainer.sh) and [`env_gpu_optimized_tf`](https://github.com/erskordi/HPC/blob/HPC-RL/languages/python/openai_rllib/simple-example-gpu/env_example_optimized_tf.yml), and they can be used as templates for future projects.
-
-## Outputs
-
-The output presents the same data as when running CPU-only experiments.
-
-The difference is that now a GPU is also utilized (`1.0/1 GPUs`). This is the easiest way to make sure that a GPU was indeed allocated:
-```
-== Status ==
-Memory usage on this node: 6.5/92.8 GiB
-Using FIFO scheduling algorithm.
-Resources requested: 108.0/180 CPUs, 1.0/1 GPUs, 0.0/807.51 GiB heap, 0.0/294.94 GiB objects (0.0/1.0 accelerator_type:V100)
-Result logdir: /scratch/eskordil/ray_results/CartPole-v0
-Number of trials: 1/1 (1 RUNNING)
-+-----------------------------+----------+------------------+--------+------------------+-------+----------+----------------------+----------------------+--------------------+
-| Trial name                  | status   | loc              |   iter |   total time (s) |    ts |   reward |   episode_reward_max |   episode_reward_min |   episode_len_mean |
-|-----------------------------+----------+------------------+--------+------------------+-------+----------+----------------------+----------------------+--------------------|
-| PPO_CartPole-v0_0339b_00000 | RUNNING  | 10.148.8.36:3651 |	   1 |          11.9292 | 21400 |  22.1782 |                   84 |                    8 |            22.1782 |
-+-----------------------------+----------+------------------+--------+------------------+-------+----------+----------------------+----------------------+--------------------+
-
-
-Result for PPO_CartPole-v0_0339b_00000:
-  agent_timesteps_total: 42800
-  custom_metrics: {}
-  date: 2021-05-12_10-00-28
-  done: false
-  episode_len_mean: 47.06188118811881
-  episode_media: {}
-  episode_reward_max: 200.0
-  episode_reward_mean: 47.06188118811881
-  episode_reward_min: 9.0
-  episodes_this_iter: 404
-  episodes_total: 1302
-  experiment_id: f5a33b1e020c4ef19dd38f1ab425b16d
-  hostname: r103u23
-  info:
-    learner:
-      default_policy:
-        learner_stats:
-          cur_kl_coeff: 0.30000001192092896
-          cur_lr: 4.999999873689376e-05
-          entropy: 0.5984720587730408
-          entropy_coeff: 0.0
-          kl: 0.01920320838689804
-          model: {}
-          policy_loss: -0.030758701264858246
-          total_loss: 394.77838134765625
-          vf_explained_var: 0.14716656506061554
-          vf_loss: 394.8033447265625
-    num_agent_steps_sampled: 42800
-    num_steps_sampled: 42800
-    num_steps_trained: 42800
-  iterations_since_restore: 2
-  node_ip: 10.148.8.36
-  num_healthy_workers: 107
-  off_policy_estimator: {}
-  perf:
-    cpu_util_percent: 6.346666666666667
-    ram_util_percent: 3.200000000000001
-  pid: 3651
-  policy_reward_max: {}
-  policy_reward_mean: {}
-  policy_reward_min: {}
-  sampler_perf:
-    mean_action_processing_ms: 0.04437368377214986
-    mean_env_render_ms: 0.0
-    mean_env_wait_ms: 0.056431942853082055
-    mean_inference_ms: 0.7302830909252791
-    mean_raw_obs_processing_ms: 0.06735290716566901
-  time_since_restore: 22.6750385761261
-  time_this_iter_s: 10.745835304260254
-  time_total_s: 22.6750385761261
-  timers:
-    learn_throughput: 1949.331
-    learn_time_ms: 10978.124
-    sample_throughput: 92888.12
-    sample_time_ms: 230.385
-    update_time_ms: 9.555
-  timestamp: 1620835228
-  timesteps_since_restore: 0
-  timesteps_total: 42800
-  training_iteration: 2
-  trial_id: 0339b_00000
-```
diff --git a/languages/python/RLlib/simple-example-gpu/env_example_gpu.yml b/languages/python/RLlib/simple-example-gpu/env_example_gpu.yml
deleted file mode 100644
index 44beb3a75..000000000
--- a/languages/python/RLlib/simple-example-gpu/env_example_gpu.yml
+++ /dev/null
@@ -1,20 +0,0 @@
-name: env_example_gpu
-channels:
-  - conda-forge
-  - defaults
-dependencies:
-  - python=3.7
-  - gym
-  - jupyterlab
-  - pandas
-  - scikit-learn
-  - numpy=1.19.5
-  - jupyter
-  - notebook
-  - cudatoolkit-dev
-  - cudnn=7.6
-  - pip
-  - pip:
-    - ray[rllib,tune,debug]
-    - tensorflow-gpu==2.3.0
-    - tensorflow-addons
diff --git a/languages/python/RLlib/simple-example-gpu/env_example_optimized_tf.yml b/languages/python/RLlib/simple-example-gpu/env_example_optimized_tf.yml
deleted file mode 100644
index 03f389636..000000000
--- a/languages/python/RLlib/simple-example-gpu/env_example_optimized_tf.yml
+++ /dev/null
@@ -1,15 +0,0 @@
-name: env_gpu_optimized_tf
-channels:
-  - conda-forge
-  - defaults
-dependencies:
-  - python=3.7
-  - anaconda::tensorflow==2.2.0
-  - gym
-  - pandas
-  - pip
-  - pip:
-    - numpy==1.18.5
-    - scipy==1.4.1
-    - ai-benchmark
-    - ray[rllib,tune,debug]
diff --git a/languages/python/RLlib/simple-example-gpu/gpu_trainer.sh b/languages/python/RLlib/simple-example-gpu/gpu_trainer.sh
deleted file mode 100644
index 599e242c1..000000000
--- a/languages/python/RLlib/simple-example-gpu/gpu_trainer.sh
+++ /dev/null
@@ -1,77 +0,0 @@
-#!/bin/bash --login
-
-# Basic options
-#SBATCH --account=rlldrd
-#SBATCH --job-name=cartpole-gpus
-#SBATCH --time=00:10:00
-
-# Ray head node
-#SBATCH --nodes=1
-#SBATCH --tasks-per-node=1
-
-# Rollout nodes - Nodes with multiple runs of OpenAI Gym 
-#SBATCH hetjob
-#SBATCH --nodes=3
-#SBATCH --tasks-per-node=1
-#SBATCH --cpus-per-task=36
-
-# Policy training node - This is the GPU node
-#SBATCH hetjob
-#SBATCH --nodes=1
-#SBATCH --tasks-per-node=1
-#SBATCH --partition=debug
-#SBATCH --gres=gpu:1
-
-env
-
-## Never forget to unset LD_PRELOAD before executing your code
-module purge
-conda activate /projects/rlldrd/eskordil/envs/env_example_gpu_2
-unset LD_PRELOAD
-
-# Get nodes 
-head_node=$(scontrol show hostnames $SLURM_JOB_NODELIST_HET_GROUP_0)
-rollout_nodes=$(scontrol show hostnames $SLURM_JOB_NODELIST_HET_GROUP_1)
-rollout_nodes_array=( $rollout_nodes )
-learner_node=$(scontrol show hostnames $SLURM_JOB_NODELIST_HET_GROUP_2)
-echo "head node    : "$head_node
-echo "rollout nodes: "$rollout_nodes
-echo "learner node : "$learner_node
-
-rollout_node_num=$(( $SLURM_JOB_NUM_NODES_HET_GROUP_1 ))
-rollout_num_cpus=$(( $rollout_node_num * $SLURM_CPUS_ON_NODE ))
-echo "rollout num cpus: "$rollout_num_cpus
-
-ip_prefix=$(srun --pack-group=0 --nodes=1 --ntasks=1 -w $head_node hostname --ip-address) # Making address
-port=6379
-ip_head=$ip_prefix:$port
-redis_password=$(uuidgen)
-echo "ip_prefix: "$ip_prefix
-echo "suffix: "$port
-echo "ip_head: "$ip_head
-echo "redis_password: "$redis_password
-
-export ip_head # Exporting for latter access by trainer.py
-
-echo "starting head node at $head_node"
-srun --pack-group=0 --nodes=1 --ntasks=1 -w $head_node ray start --block --head \
---node-ip-address="$ip_prefix" --port=$port --redis-password=$redis_password & # Starting the head
-sleep 10
-
-echo "starting rollout workers"
-for ((  i=0; i<$rollout_node_num; i++ ))
-do
-  rollout_node=${rollout_nodes_array[$i]}
-  echo "i=${i}, rollout_node=${rollout_node}"
-  srun --pack-group=1 --nodes=1 --ntasks=1 -w $rollout_node \
-   ray start --block --address "$ip_head" --redis-password=$redis_password & # Starting the workers
-  sleep 5
-done
-
-echo "starting learning on GPU"
-srun --pack-group=2 --nodes=1 --gres=gpu:1 -w $learner_node ray start --block --address "$ip_head" --redis-password=$redis_password &
-
-sleep 10
-
-echo "calling simple_trainer.py"
-python -u simple_trainer.py --redis-password $redis_password --num-cpus $rollout_num_cpus --num-gpus 1
diff --git a/languages/python/RLlib/simple-example-gpu/gpu_trainer_optimized_tf.sh b/languages/python/RLlib/simple-example-gpu/gpu_trainer_optimized_tf.sh
deleted file mode 100644
index aa3bf1fd6..000000000
--- a/languages/python/RLlib/simple-example-gpu/gpu_trainer_optimized_tf.sh
+++ /dev/null
@@ -1,82 +0,0 @@
-#!/bin/bash --login
-
-# Basic options
-#SBATCH --account=rlldrd
-#SBATCH --job-name=cartpole-gpus
-#SBATCH --time=00:10:00
-
-# Ray head node
-#SBATCH --nodes=1
-#SBATCH --tasks-per-node=1
-
-# Rollout nodes - Nodes with multiple runs of OpenAI Gym 
-#SBATCH hetjob
-#SBATCH --nodes=3
-#SBATCH --tasks-per-node=1
-#SBATCH --cpus-per-task=36
-
-# Policy training node - This is the GPU node
-#SBATCH hetjob
-#SBATCH --nodes=1
-#SBATCH --tasks-per-node=1
-#SBATCH --partition=debug
-#SBATCH --gres=gpu:1
-
-env
-
-## Never forget to unset LD_PRELOAD before executing your code
-module purge
-module use /nopt/nrel/apps/modules/centos74/modulefiles/
-module load gcc/7.4.0
-module load cuda/10.0.130
-module load cudnn/7.4.2/cuda-10.0
-module load conda
-conda activate /projects/rlldrd/eskordil/envs/env_example_optimized_tf
-unset LD_PRELOAD
-
-# Get nodes 
-head_node=$(scontrol show hostnames $SLURM_JOB_NODELIST_HET_GROUP_0)
-rollout_nodes=$(scontrol show hostnames $SLURM_JOB_NODELIST_HET_GROUP_1)
-rollout_nodes_array=( $rollout_nodes )
-learner_node=$(scontrol show hostnames $SLURM_JOB_NODELIST_HET_GROUP_2)
-echo "head node    : "$head_node
-echo "rollout nodes: "$rollout_nodes
-echo "learner node : "$learner_node
-
-rollout_node_num=$(( $SLURM_JOB_NUM_NODES_HET_GROUP_1 ))
-rollout_num_cpus=$(( $rollout_node_num * $SLURM_CPUS_ON_NODE ))
-echo "rollout num cpus: "$rollout_num_cpus
-
-ip_prefix=$(srun --pack-group=0 --nodes=1 --ntasks=1 -w $head_node hostname --ip-address) # Making address
-port=6379
-ip_head=$ip_prefix:$port
-redis_password=$(uuidgen)
-echo "ip_prefix: "$ip_prefix
-echo "suffix: "$port
-echo "ip_head: "$ip_head
-echo "redis_password: "$redis_password
-
-export ip_head # Exporting for latter access by trainer.py
-
-echo "starting head node at $head_node"
-srun --pack-group=0 --nodes=1 --ntasks=1 -w $head_node ray start --block --head \
---node-ip-address="$ip_prefix" --port=$port --redis-password=$redis_password & # Starting the head
-sleep 10
-
-echo "starting rollout workers"
-for ((  i=0; i<$rollout_node_num; i++ ))
-do
-  rollout_node=${rollout_nodes_array[$i]}
-  echo "i=${i}, rollout_node=${rollout_node}"
-  srun --pack-group=1 --nodes=1 --ntasks=1 -w $rollout_node \
-   ray start --block --address "$ip_head" --redis-password=$redis_password & # Starting the workers
-  sleep 5
-done
-
-echo "starting learning on GPU"
-srun --pack-group=2 --nodes=1 --gres=gpu:1 -w $learner_node ray start --block --address "$ip_head" --redis-password=$redis_password &
-
-sleep 10
-
-echo "calling simple_trainer.py"
-python -u simple_trainer.py --redis-password $redis_password --num-cpus $rollout_num_cpus --num-gpus 1
diff --git a/languages/python/RLlib/simple-example-gpu/simple_trainer.py b/languages/python/RLlib/simple-example-gpu/simple_trainer.py
deleted file mode 100644
index 078c74ae0..000000000
--- a/languages/python/RLlib/simple-example-gpu/simple_trainer.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import argparse
-import os
-
-import ray
-from ray import tune
-
-###############################################
-## Command line args
-###############################################
-parser = argparse.ArgumentParser(description="Script for training RLLIB agents")
-parser.add_argument("--num-cpus", type=int, default=0)
-parser.add_argument("--num-gpus", type=int, default=0)
-parser.add_argument("--name-env", type=str, default="CartPole-v0")
-parser.add_argument("--run", type=str, default="PPO")
-parser.add_argument("--redis-password", type=str, default=None)
-parser.add_argument("--local-mode", action="store_true")
-args = parser.parse_args()
-print(args)
-
-################################################
-## RLLIB SETUP
-################################################
-if args.redis_password is None:
-    # Single node
-    ray.init(local_mode=args.local_mode)
-    num_cpus = args.num_cpus - 1
-else:
-    # On a cluster
-    ray.init(_redis_password=args.redis_password, address=os.environ["ip_head"])
-    num_cpus = args.num_cpus - 1
-
-######################################
-## Run TUNE Experiments!
-######################################
-tune.run(
-    args.run,
-    name=args.name_env,
-    local_dir="/scratch/$USER/ray_results",
-    stop={"training_iteration": 100},
-    config={
-        "env": args.name_env,
-        "framework": "tf",
-        "num_workers": num_cpus, 
-        "num_gpus": args.num_gpus,
-        "ignore_worker_failures": True
-        }
-    )
diff --git a/languages/python/RLlib/simple-example/README.md b/languages/python/RLlib/simple-example/README.md
deleted file mode 100644
index 896167017..000000000
--- a/languages/python/RLlib/simple-example/README.md
+++ /dev/null
@@ -1,413 +0,0 @@
-# Agent training with Ray/RLlib
-
-RL algorithms are notorious for the amount of data they need to collect in order to learn policies. The more data collected, the better the training will be. The best way to do it is to run many Gym instances in parallel and collecting experience, and this is where RLlib assists.
-
-[RLlib](https://docs.ray.io/en/master/rllib.html) is an open-source library for reinforcement learning that offers both high scalability and a unified API for a variety of applications. It supports all known deep learning frameworks such as Tensorflow, Pytorch, although most parts are framework-agnostic and can be used by either one.
-
-The RL policy learning examples provided in this tutorial demonstrate the RLlib abilities. For convenience, the `CartPole-v0` OpenAI Gym environment will be used.
-
-The most straightforward way is to create a Python "trainer" script. It will call the necessary packages, setup flags, and run the experiments, all nicely put in a few lines of Python code.
-
-## Import packages
-
-Begin trainer by importing the `ray` package:
-```python
-import ray
-from ray import tune
-```
-`Ray` consists of an API readily available for building [distributed applications](https://docs.ray.io/en/master/index.html). On top of it, there are several problem-solving libraries, one of which is RLlib.
-
-`Tune` is another one of `Ray`'s libraries for scalable hyperparameter tuning. All RLlib trainers (scripts for RL agent training) are compatible with Tune API, making experimenting easy and streamlined.
-
-Import also the `argparse` package and setup some flags. Although that step is not mandatory, these flags will allow controlling of certain hyperparameters, such as:
-* RL algorithm utilized (e.g. PPO, DQN)
-* Number of CPUs/GPUs
-* ...and others
-```python
-import argparse
-```
-
-## Create flags
-Begin by defining the following flags:
-```python
-parser.add_argument("--num-cpus", type=int, default=0)
-parser.add_argument("--num-gpus", type=int, default=0)
-parser.add_argument("--name-env", type=str, default="CartPole-v0")
-parser.add_argument("--run", type=str, default="DQN")
-parser.add_argument("--local-mode", action="store_true")
-```
-All of them are self-explanatory, however let's see each one separately.
-1. `--num-cpus`: Defines the number of CPU cores used for experience collection (Default value 0 means allocation of a single CPU core).
-2. `--num-gpus`: Allocates a GPU node for policy learning (works only for Tensorflow-GPU). Except whole values (1,2,etc.), it also accepts partial values, in case 100% of the GPU is not necessary.
-3. `--name-env`: The name of the OpenAI Gym environment.
-4. `--run`: Specifies the RL algorithm for agent training.
-5. `--local-mode`: Helps defining whether experiments running on a single core or multiple cores.
-
-## Initialize Ray
-
-Ray is able to run either on a local mode (e.g. laptop, personal computer), or on a cluster. Depending on the nature of the experiment, distinguish between the two modes using an `if-else` statement:
-```python
-if args.redis_password is None:
-    # Single node
-    ray.init(local_mode=args.local_mode)
-    num_cpus = args.num_cpus - 1
-else:
-    # On a cluster
-    ray.init(_redis_password=args.redis_password, address=os.environ["ip_head"])
-    num_cpus = args.num_cpus - 1
-```
-*Do not worry about the `redis_password` for now, it will be explained later in the tutorial*
-
-For the first experiment, only a single core is needed. Therefore, setup ray to run on a local mode: `ray.init(local_mode=args.local_mode)`. The next line shows the number of CPU cores to be used. Remember that RLlib always allocates one CPU core, even when `--num-cpus=0`. Hence, always subtract one from the total number of cores.
-
-## Run experiments with Tune
-
-This is the final step in this basic trainer. Tune's `tune.run` function initiates the agent training process. There are three main arguments in this function:
-* RL algorithm (string): It is defined in the `--run` flag (PPO, DQN, etc.).
-* `stop` (dictionary): Provides a criterion to stop training (in this example is the number of training iterations; stop training when iterations reach 10,000).
-* `config` (dictionary): Basic information for training, contains the OpenAI Gym environment name, number of CPUs/GPUs, and others.
-```python
-tune.run(
-    args.run,
-    name=args.name_env,
-    stop={"training_iteration": 10000},
-    config={
-        "env": args.name_env,
-        "num_workers": args.num_cpus, 
-        "num_gpus": args.num_gpus,
-        "ignore_worker_failures": True
-        }
-    )
-```
-That's it! The RLlib trainer is ready!
-
-Note here that, except default hyperparameters like those above, [every RL algorithm](https://docs.ray.io/en/master/rllib-algorithms.html#available-algorithms-overview) provided by RLlib has its own hyperparameters and their default values that can be tuned in advance.
-
-The code of the trainer in this example can be found [in the repo](https://github.com/erskordi/HPC/blob/HPC-RL/languages/python/openai_rllib/simple-example/simple_trainer.py).
-
-
-# Run experiments on Eagle
-
-The next step calls for running the trainer script on Eagle.
-
-## Allocate an interactive Eagle node
-
-First, allocate an interactive node. Let's start by allocating a `debug` node. Debug nodes have a maximum allocation time of one hour (60 minutes), but they are easier to allocate than regular nodes:
-```
-srun -N 1 -t 1:00:00 -A <project_name> --partition debug --pty $SHELL
-```
-Successful node allocation is shown as:
-```
-srun: job 6896853 queued and waiting for resources
-srun: job 6896853 has been allocated resources
-```
-Activate the Anaconda environment:
-```
-module purge
-conda activate env_example
-```
-**VERY IMPORTANT!!** 
-Before the experiment, run
-```
-unset LD_PRELOAD
-```
-For communication between cores in a node (and between nodes in multi-node experiments), RLlib uses a Redis server. However, there is some kind of process running on Eagle causing Redis server to malfunction. Therefore, make sure you unset variable `LD_PRELOAD`, which disables that process and lets your experiment run smoothly.
-
-## Run multi-core experiments
-
-The previous example is designed to run on a single CPU core. However, as explained above, RL training is highly benefited from running multiple concurrent OpenAI Gym rollouts. A single node on Eagle has 36 CPU cores, therefore use any number of those in order to speed up your agent training. 
-
-For all 36 cores, adjust the `--num-cpus` hyperparameter to reflect to all CPUs on the node:
-```
-python simple_trainer.py --num-cpus 35
-```
-Again, RLlib by default utilizes a single CPU core, therefore by putting `--num-cpus` equal to 35 means that all 36 cores are requested.
-
-Such is not the case with the `num_gpus` key, where zero means no GPU allocation is permitted. This is because GPUs are used for policy training and not running the OpenAI Gym environment instances, thus they are not mandatory (although having a GPU node can assist the agent training by reducing training time).
-
-
-## Run multi-node/multi-core experiments
-
-Problems that involve highly complex environments with very large observation and/or action state spaces will probably require running experiments utilizing more than one Eagle nodes. In this case it is better to work with slurm scripts. You can submit such scripts as 
-```
-sbatch <name_of_your_batch_script>
-```
-The results are exported in an `slurm-<job_id>.out` file. This file can be accesssed:
- * During training (`tail -f slurm-<job_id>.out`) 
- * Open it using a standard text editor (e.g. `nano`) after training is finished.
-
-An example of a `slurm-<job_id>.out` file is also included in the repo for reference.
-
-## Example: CartPole-v0
-
-As explained above, CartPole is a rather simple environment and solving it using multiple cores on a single node feels like an overkill, let alone multiple nodes! However, it is a good example for giving you an experience on running RL experiments using RLlib.
-
-For multiple nodes it is more convenient to use a slurm script instead of an interactive node. Slurm files are submitted as `sbatch <name_of_your_batch_script>`, and the results are exported in an `slurm-<job_id>.out` file. The `.out` file can be interactively accessed during training using the `tail -f slurm-<job_id>.out` command. Otherwise, after training, open it using a standard text editor (e.g. `nano`).
-Next, the basic parts of the slurm script file are given. The repo also provides [the complete script](https://github.com/erskordi/HPC/blob/HPC-RL/languages/python/openai_rllib/multi_node_trainer.sh).
-
-The slurm file begins with defining some basic `SBATCH` options, including the desired training time, number of nodes, tasks per node, etc.
-
-```bash
-#!/bin/bash --login
-
-#SBATCH --job-name=cartpole-multiple-nodes
-#SBATCH --time=00:10:00
-#SBATCH --nodes=3
-#SBATCH --tasks-per-node=1
-#SBATCH --cpus-per-task=36
-#SBATCH --account=A<account>
-env
-```
-Allocating multiple nodes means creating a Ray cluster. A Ray cluster consists of a head node and a set of worker nodes. The head node needs to be started first, and the worker nodes are given the address of the head node to form the cluster. 
-
-The agent training will run for 20 minutes (`SBATCH --time=00:20:00`), and on three Eagle CPU nodes (`SBATCH --nodes=3`). Every node will execute a single task (`SBATCH --tasks-per-node=1`), which will be executed on all 36 cores (`SBATCH --cpus-per-task=36`). Then, define the project account. Other options are also available, such as whether to prioritize the experiment (`--qos=high`).
-
-Afterwards, use the commands to activate the Anaconda environment. Do not forget to `unset LD_PRELOAD`.
-```batch
-module purge
-conda activate /scratch/$USER/conda-envs/env_example
-unset LD_PRELOAD
-```
-Next, set up the Redis server that will allow all the nodes you requested to communicate with each other. For that, set a Redis password:
-```batch
-ip_prefix=$(srun --nodes=1 --ntasks=1 -w $node1 hostname --ip-address)
-port=6379
-ip_head=$ip_prefix:$port
-redis_password=$(uuidgen)
-```
-Then, submit the jobs one at a time at the workers, starting with the head node and moving on to the rest of them.
-```batch
-srun --nodes=1 --ntasks=1 -w $node1 ray start --block --head \
---node-ip-address="$ip_prefix" --port=$port --redis-password=$redis_password &
-sleep 10
-
-echo "starting workers"
-for ((  i=1; i<=$worker_num; i++ ))
-do
-  node2=${nodes_array[$i]}
-  echo "i=${i}, node2=${node2}"
-  srun --nodes=1 --ntasks=1 -w $node2 ray start --block --address "$ip_head" --redis-password=$redis_password &
-  sleep 5
-done
-```
-Finally, set the Python script to run. Since this experiment will run on a cluster, Ray will be initialized as:
-```python
-ray.init(_redis_password=args.redis_password, address=os.environ["ip_head"])
-num_cpus = args.num_cpus - 1
-```
-Therefore, `--redis-password` option must be active, along with the total number of CPUs:
-```batch
-python -u simple_trainer.py --redis-password $redis_password --num-cpus $total_cpus
-```
-The experiment is ready to begin, simply run:
-```
-sbatch <your_slurm_file>
-```
-If the trainer script is on a different directory, make sure to `cd` to this directory in the slurm script before executing it.
-```
-# Example where the trainer is on scratch:
-cd /scratch/$USER/path_to_specific_directory
-python -u simple_trainer.py --redis-password $redis_password --num-cpus $total_cpus
-```
-
-# Outputs (single-core/multiple-core)
-
-RLlib produces outputs of the following form:
-```
-== Status ==
-Memory usage on this node: 9.1/187.3 GiB
-Using FIFO scheduling algorithm.
-Resources requested: 1/36 CPUs, 0/0 GPUs, 0.0/119.73 GiB heap, 0.0/38.13 GiB objects
-Result logdir: /home/eskordil/ray_results/CartPole-v0
-Number of trials: 1 (1 RUNNING)
-+-----------------------------+----------+-------+
-| Trial name                  | status   | loc   |
-|-----------------------------+----------+-------|
-| DQN_CartPole-v0_380be_00000 | RUNNING  |       |
-+-----------------------------+----------+-------+
-
-
-(pid=200639) 2021-04-29 10:44:55,555	INFO trainer.py:585 -- Tip: set framework=tfe or the --eager flag to enable TensorFlow eager execution
-(pid=200639) 2021-04-29 10:44:55,555	INFO trainer.py:612 -- Current log_level is WARN. For more information, set 'log_level': 'INFO' / 'DEBUG' or use the -v and -vv flags.
-(pid=200639) 2021-04-29 10:44:56,504	WARNING util.py:37 -- Install gputil for GPU system monitoring.
-Result for DQN_CartPole-v0_380be_00000:
-  custom_metrics: {}
-  date: 2021-04-29_10-44-57
-  done: false
-  episode_len_mean: 22.906976744186046
-  episode_reward_max: 86.0
-  episode_reward_mean: 22.906976744186046
-  episode_reward_min: 8.0
-  episodes_this_iter: 43
-  episodes_total: 43
-  experiment_id: 66ccec197de8447fb178c8abebd26107
-  experiment_tag: '0'
-  hostname: r7i7n35
-  info:
-    last_target_update_ts: 1000
-    learner:
-      default_policy:
-        cur_lr: 0.0005000000237487257
-        max_q: 0.3239544630050659
-        mean_q: -0.08293987810611725
-        mean_td_error: -1.207885503768921
-        min_q: -1.2971210479736328
-        model: {}
-    num_steps_sampled: 1000
-    num_steps_trained: 32
-    num_target_updates: 1
-  iterations_since_restore: 1
-  node_ip: 10.148.7.231
-  num_healthy_workers: 0
-  off_policy_estimator: {}
-  perf:
-    cpu_util_percent: 2.85
-    ram_util_percent: 5.0
-  pid: 200639
-  policy_reward_max: {}
-  policy_reward_mean: {}
-  policy_reward_min: {}
-  sampler_perf:
-    mean_env_wait_ms: 0.042502815787727896
-    mean_inference_ms: 0.49455801804701643
-    mean_processing_ms: 0.1114586611965915
-  time_since_restore: 0.9389147758483887
-  time_this_iter_s: 0.9389147758483887
-  time_total_s: 0.9389147758483887
-  timers:
-    learn_throughput: 197.465
-    learn_time_ms: 162.054
-  timestamp: 1619714697
-  timesteps_since_restore: 0
-  timesteps_total: 1000
-  training_iteration: 1
-  trial_id: 380be_00000
-```
-Most of this information will not be necessary, however there are some parts that give an idea of the results quality.
-
-One important piece of information is the utilization of resources achieved by RLlib during training, as well as the RL algorithm used for the experiment:
-```
-Resources requested: 1/36 CPUs, 0/0 GPUs, 0.0/119.73 GiB heap, 0.0/38.13 GiB objects
-Result logdir: /home/eskordil/ray_results/CartPole-v0
-Number of trials: 1 (1 RUNNING)
-+-----------------------------+----------+---------------------+--------+------------------+------+----------+
-| Trial name                  | status   | loc                 |   iter |   total time (s) |   ts |   reward |
-|-----------------------------+----------+---------------------+--------+------------------+------+----------|
-| DQN_CartPole-v0_380be_00000 | RUNNING  | 10.148.7.231:200639 |      2 |          2.55838 | 2000 |    19.69 |
-+-----------------------------+----------+---------------------+--------+------------------+------+----------+
-```
-These lines show the following:
-* Number of CPUs utilized: If `--num-cpus` was defined as equal to zero, then by default RLlib acquired a single CPU core to run the experiment.
-* No GPU resources were utilized: Expected since `--num-gpus` was set to zero.
-* Trial name: Generated automatically, it gives information regarding the RL algorithm used (here DQN) and the OpenAI Gym environment.
-* status: either RUNNING or FAILED.
-* iter: Current training iteration.
-* total time (seconds): time spend per iteration.
-* reward: The reward returned after the end of this iteration. Succesfull agent training will be shown through increase of that value during training.
-
-
-This is the most basic, but in the same time important, information after every training iteration. The rest of this snippet contains more specialized information, but most of it will not be relevant in subsequent analysis. 
-
-When `--num-cpus` equals 35, then the aforementioned printout will be:
-```
-== Status ==
-Memory usage on this node: 10.6/92.8 GiB
-Using FIFO scheduling algorithm.
-Resources requested: 36/36 CPUs, 0/0 GPUs, 0.0/54.25 GiB heap, 0.0/18.7 GiB objects
-Result logdir: /home/eskordil/ray_results/CartPole-v0
-Number of trials: 1 (1 RUNNING)
-+-----------------------------+----------+-----------------+--------+------------------+------+----------+
-| Trial name                  | status   | loc             |   iter |   total time (s) |   ts |   reward |
-|-----------------------------+----------+-----------------+--------+------------------+------+----------|
-| DQN_CartPole-v0_b665a_00000 | RUNNING  | 127.0.0.1:92844 |      3 |          6.30516 | 3360 |    19.07 |
-+-----------------------------+----------+-----------------+--------+------------------+------+----------+
-
-
-Result for DQN_CartPole-v0_b665a_00000:
-custom_metrics: {}
-date: 2021-04-29_09-16-00
-done: false
-episode_len_mean: 17.37
-episode_reward_max: 97.0
-episode_reward_mean: 17.37
-episode_reward_min: 8.0
-episodes_this_iter: 75
-episodes_total: 233
-experiment_id: 573f3799c81946439a598d6d633df7d4
-experiment_tag: '0'
-hostname: r4i7n35
-info:
-last_target_update_ts: 4480
-learner:
-  default_policy:
-    cur_lr: 0.0005000000237487257
-    max_q: 12.753413200378418
-    mean_q: 5.389695167541504
-    mean_td_error: -1.9424524307250977
-    min_q: 0.2038756161928177
-    model: {}
-num_steps_sampled: 4480
-num_steps_trained: 800
-num_target_updates: 7
-iterations_since_restore: 4
-node_ip: 127.0.0.1
-num_healthy_workers: 35
-off_policy_estimator: {}
-perf:
-cpu_util_percent: 7.4
-ram_util_percent: 11.4
-pid: 92844
-policy_reward_max: {}
-policy_reward_mean: {}
-policy_reward_min: {}
-sampler_perf:
-mean_env_wait_ms: 0.04555504737231483
-mean_inference_ms: 0.8442203457250892
-mean_processing_ms: 0.1175659340957767
-time_since_restore: 8.04318118095398
-time_this_iter_s: 1.7380259037017822
-time_total_s: 8.04318118095398
-timers:
-learn_throughput: 19616.739
-learn_time_ms: 1.631
-update_time_ms: 89.14
-timestamp: 1619709360
-timesteps_since_restore: 0
-timesteps_total: 4480
-training_iteration: 4
-trial_id: b665a_00000
-```
-Obviously, RLlib here utilized all CPU cores on the node (36/36). 
-
-It might seem odd that the `total time(s)` here is slightly more than when using a single CPU core, but this happens because when the algorithm runs 36 instances of the OpenAI Gym environment concurrently, more data are collected for policy training leading to faster reward convergence. In any case, these times are not absolute, and will decrease during training, especially as the agent approaches the optimal reward value.
-
-## Metadata
-
-RLlib creates a directory named `ray_results` at `home` directory that Ray uses to dump metadata for all experiments and contains distilled information from all training processes. These results can be used later for evaluating the quality of training. 
-
-After training is finished, go to home directory:
-```
-cd ~/
-``` 
-Then, type `cd ray_results`. There will be directories named after every OpenAI Gym environment used for experimenting. Hence, for CartPole there will be a directory named `CartPole-v0`. Within this directory, there will be subdirectories with names being combinations of the RL algorithm used for training, the OpenAI Gym environment's name, the datetime when the experiment took place, and a unique string. 
-
-So, an experiment for CartPole, using Deep Q-Network (DQN), and started on April 29, 2021, at 9:14:57AM, will have a subdirectory containing the metadata like this:
-```
-DQN_CartPole-v0_0_2021-04-29_09-14-573vmq2rio
-```
-In that directory, there will be various text, JSON, and CSV files. One of them, named `progress.csv` contains a dataframe with columns such as `episode_reward_mean`, that helps to evaluate the quality of the training process.
-
-## Comparisons
-
-While not a mandatory part of the tutorial, it is interesting to compare the outcomes from running experiments on a single core versus on all cores on a single Eagle node. One approach is comparing the values from column `episode_reward_mean` in files `progress.csv`. These values show how fast (or not) the reward converged to the optimal value during agent training. The faster the convergence, the better.
-
-The following image shows the agent training progress, in terms of reward convergence, for the `CartPole-v0` environment. The RL algorithm used was the [Proximal Policy Optimization](https://arxiv.org/pdf/1707.06347.pdf), and training was conducted for 100 iterations.
-<p float="left">
-  <img src="images/ppo_rew_comparison.png" width="700" />
-</p>
-Obviously, training using all CPU cores on a node led to faster convergence to the optimal value. 
-
-It is necessary to say here that CartPole is a simple environment where the optimal reward value (200) can be easily reached even when using a single CPU core on a personal computer. The power of using multiple cores becomes more apparent in cases of more complex environments (such as the [Atari environments](https://gym.openai.com/envs/#atari)). RLlib website also gives examples of the [scalability benefits](https://docs.ray.io/en/master/rllib-algorithms.html#ppo) for many state-of-the-art RL algorithms.
-
-**Supplemental notes:**
-As you noticed, when using RLlib for RL traning, there is no need to `import gym`, as we did in the non-training example, because RLlib recognizes automatically all benchmark OpenAI Gym environments. Even when you create your own custom-made Gym environments, RLlib provides proper functions with which you can register your environment before training.
diff --git a/languages/python/RLlib/simple-example/images/ppo_rew_comparison.png b/languages/python/RLlib/simple-example/images/ppo_rew_comparison.png
deleted file mode 100644
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diff --git a/languages/python/RLlib/simple-example/multi_node_trainer.sh b/languages/python/RLlib/simple-example/multi_node_trainer.sh
deleted file mode 100644
index 77f58b565..000000000
--- a/languages/python/RLlib/simple-example/multi_node_trainer.sh
+++ /dev/null
@@ -1,59 +0,0 @@
-#!/bin/bash --login
-
-#SBATCH --job-name=cartpole-multiple-nodes
-#SBATCH --time=00:10:00
-#SBATCH --nodes=3
-#SBATCH --tasks-per-node=1
-#SBATCH --cpus-per-task=36
-#SBATCH --account=rlldrd
-env
-
-## Never forget to unset LD_PRELOAD before executing your code
-module purge
-conda activate /projects/rlldrd/eskordil/envs/env_example
-unset LD_PRELOAD
-
-# number of nodes other than the head node, hence minus one
-worker_num=$(( $SLURM_JOB_NUM_NODES - 1 ))
-total_cpus=$(( $worker_num * $SLURM_CPUS_ON_NODE ))
-echo "worker_num="$worker_num
-echo "total_cpus="$total_cpus
-
-nodes=$(scontrol show hostnames $SLURM_JOB_NODELIST) # Getting the node names
-nodes_array=( $nodes )
-
-echo "nodes: "$nodes
-echo "nodes_array: "$nodes_array
-
-node1=${nodes_array[0]}
-echo "node1: "$node1
-
-ip_prefix=$(srun --nodes=1 --ntasks=1 -w $node1 hostname --ip-address) # Making address
-port=6379
-ip_head=$ip_prefix:$port
-redis_password=$(uuidgen)
-echo "ip_prefix: "$ip_prefix
-echo "suffix: "$port
-echo "ip_head: "$ip_head
-echo "redis_password: "$redis_password
-
-export ip_head # Exporting for latter access by trainer.py
-
-echo "starting head at $node1"
-srun --nodes=1 --ntasks=1 -w $node1 ray start --block --head \
---node-ip-address="$ip_prefix" --port=$port --redis-password=$redis_password & # Starting the head
-sleep 10
-
-echo "starting workers"
-for ((  i=1; i<=$worker_num; i++ ))
-do
-  node2=${nodes_array[$i]}
-  echo "i=${i}, node2=${node2}"
-  srun --nodes=1 --ntasks=1 -w $node2 ray start --block --address "$ip_head" --redis-password=$redis_password & # Starting the workers
-  sleep 5
-done
-
-sleep 10
-
-echo "calling simple_trainer.py"
-python -u simple_trainer.py --redis-password $redis_password --num-cpus $total_cpus
diff --git a/languages/python/RLlib/simple-example/simple_trainer.py b/languages/python/RLlib/simple-example/simple_trainer.py
deleted file mode 100644
index 1029b1698..000000000
--- a/languages/python/RLlib/simple-example/simple_trainer.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import argparse
-import os
-
-import ray
-from ray import tune
-
-###############################################
-## Command line args
-###############################################
-parser = argparse.ArgumentParser(description="Script for training RLLIB agents")
-parser.add_argument("--num-cpus", type=int, default=0)
-parser.add_argument("--num-gpus", type=int, default=0)
-parser.add_argument("--name-env", type=str, default="CartPole-v0")
-parser.add_argument("--run", type=str, default="PPO")
-parser.add_argument("--redis-password", type=str, default=None)
-parser.add_argument("--local-mode", action="store_true")
-args = parser.parse_args()
-print(args)
-
-################################################
-## RLLIB SETUP
-################################################
-if args.redis_password is None:
-    # Single node
-    ray.init(local_mode=args.local_mode)
-    num_cpus = args.num_cpus - 1
-else:
-    # On a cluster
-    ray.init(_redis_password=args.redis_password, address=os.environ["ip_head"])
-    num_cpus = args.num_cpus - 1
-
-######################################
-## Run TUNE Experiments!
-######################################
-tune.run(
-    args.run,
-    name=args.name_env,
-    local_dir="/scratch/$USER/ray_results",
-    stop={"training_iteration": 100},
-    config={
-        "env": args.name_env,
-        "num_workers": num_cpus, 
-        "num_gpus": args.num_gpus,
-        "ignore_worker_failures": True
-        }
-    )
diff --git a/languages/python/RLlib/simple-example/slurm-6800229.out b/languages/python/RLlib/simple-example/slurm-6800229.out
deleted file mode 100644
index 757dbcb06..000000000
--- a/languages/python/RLlib/simple-example/slurm-6800229.out
+++ /dev/null
@@ -1,1326 +0,0 @@
-AS=/nopt/nrel/apps/anaconda/5.3/bin/x86_64-conda_cos6-linux-gnu-as
-SLURM_NODELIST=r9i1n[11-12,32]
-LDFLAGS=-Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,--disable-new-dtags
-AR=/nopt/nrel/apps/anaconda/5.3/bin/x86_64-conda_cos6-linux-gnu-ar
-SLURM_JOB_NAME=cartpole-multiple-nodes
-MANPATH=/nopt/slurm/current/share:/nopt/nrel/apps/anaconda/mini_py37_4.8.3/share/man:/nopt/nrel/utils/lmod/lmod/share/man:/nopt/slurm/current/share::/opt/c3/man:/opt/clmgr/man:/opt/sgi/share/man:/opt/clmgr/share/man:/opt/clmgr/lib/cm-cli/man:/opt/clmgr/man:/opt/sgi/share/man:/opt/clmgr/share/man:/opt/clmgr/lib/cm-cli/man
-RSTUDIO_WHICH_R=/nopt/nrel/apps/anaconda/5.3/bin/R
-XDG_SESSION_ID=9550
-GCC_NM=/nopt/nrel/apps/anaconda/5.3/bin/x86_64-conda_cos6-linux-gnu-gcc-nm
-SLURMD_NODENAME=r9i1n11
-SLURM_TOPOLOGY_ADDR=root.r9.r9i1n11
-SLURM_NTASKS_PER_NODE=1
-HOSTNAME=r9i1n11
-SLURM_PRIO_PROCESS=0
-SELINUX_ROLE_REQUESTED=
-SLURM_NODE_ALIASES=(null)
-SHELL=/bin/bash
-TERM=xterm-256color
-HOST=x86_64-conda_cos6-linux-gnu
-__LMOD_REF_COUNT_MODULEPATH=/nopt/nrel/utils/modulefiles/Linux:1;/nopt/nrel/utils/modulefiles/Core:1;/nopt/nrel/apps/modules/default/modulefiles:1
-NM=/nopt/nrel/apps/anaconda/5.3/bin/x86_64-conda_cos6-linux-gnu-nm
-SLURM_JOB_QOS=normal
-HISTSIZE=1000
-CPPFLAGS=-DNDEBUG -D_FORTIFY_SOURCE=2 -O2
-TMPDIR=/tmp
-SLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.node
-SSH_CLIENT=10.10.141.83 49482 22
-MODULEPATH_ROOT=/nopt/nrel/utils/modulefiles
-XALT_EXECUTABLE_TRACKING=yes
-CONDA_SHLVL=1
-SELINUX_USE_CURRENT_RANGE=
-LD_PRELOAD=/nopt/xalt/xalt/lib64/libxalt_init.so
-CONDA_PROMPT_MODIFIER=(base) 
-SIZE=/nopt/nrel/apps/anaconda/5.3/bin/x86_64-conda_cos6-linux-gnu-size
-LMOD_PKG=/nopt/nrel/utils/lmod/lmod
-RSTUDIO_WHICH_R_PREV=/nopt/nrel/apps/anaconda/5.3/bin/R
-QTDIR=/usr/lib64/qt-3.3
-LMOD_VERSION=7.8.8
-QTINC=/usr/lib64/qt-3.3/include
-SSH_TTY=/dev/pts/20
-GFORTRAN=/nopt/nrel/apps/anaconda/5.3/bin/x86_64-conda_cos6-linux-gnu-gfortran
-LC_ALL=en_US.UTF-8
-__LMOD_REF_COUNT_LOADEDMODULES=conda/mini_py37_4.8.3:1
-QT_GRAPHICSSYSTEM_CHECKED=1
-SLURM_NNODES=3
-USER=eskordil
-LS_COLORS=rs=0:di=38;5;27:ln=38;5;51:mh=44;38;5;15:pi=40;38;5;11:so=38;5;13:do=38;5;5:bd=48;5;232;38;5;11:cd=48;5;232;38;5;3:or=48;5;232;38;5;9:mi=05;48;5;232;38;5;15:su=48;5;196;38;5;15:sg=48;5;11;38;5;16:ca=48;5;196;38;5;226:tw=48;5;10;38;5;16:ow=48;5;10;38;5;21:st=48;5;21;38;5;15:ex=38;5;34:*.tar=38;5;9:*.tgz=38;5;9:*.arc=38;5;9:*.arj=38;5;9:*.taz=38;5;9:*.lha=38;5;9:*.lz4=38;5;9:*.lzh=38;5;9:*.lzma=38;5;9:*.tlz=38;5;9:*.txz=38;5;9:*.tzo=38;5;9:*.t7z=38;5;9:*.zip=38;5;9:*.z=38;5;9:*.Z=38;5;9:*.dz=38;5;9:*.gz=38;5;9:*.lrz=38;5;9:*.lz=38;5;9:*.lzo=38;5;9:*.xz=38;5;9:*.bz2=38;5;9:*.bz=38;5;9:*.tbz=38;5;9:*.tbz2=38;5;9:*.tz=38;5;9:*.deb=38;5;9:*.rpm=38;5;9:*.jar=38;5;9:*.war=38;5;9:*.ear=38;5;9:*.sar=38;5;9:*.rar=38;5;9:*.alz=38;5;9:*.ace=38;5;9:*.zoo=38;5;9:*.cpio=38;5;9:*.7z=38;5;9:*.rz=38;5;9:*.cab=38;5;9:*.jpg=38;5;13:*.jpeg=38;5;13:*.gif=38;5;13:*.bmp=38;5;13:*.pbm=38;5;13:*.pgm=38;5;13:*.ppm=38;5;13:*.tga=38;5;13:*.xbm=38;5;13:*.xpm=38;5;13:*.tif=38;5;13:*.tiff=38;5;13:*.png=38;5;13:*.svg=38;5;13:*.svgz=38;5;13:*.mng=38;5;13:*.pcx=38;5;13:*.mov=38;5;13:*.mpg=38;5;13:*.mpeg=38;5;13:*.m2v=38;5;13:*.mkv=38;5;13:*.webm=38;5;13:*.ogm=38;5;13:*.mp4=38;5;13:*.m4v=38;5;13:*.mp4v=38;5;13:*.vob=38;5;13:*.qt=38;5;13:*.nuv=38;5;13:*.wmv=38;5;13:*.asf=38;5;13:*.rm=38;5;13:*.rmvb=38;5;13:*.flc=38;5;13:*.avi=38;5;13:*.fli=38;5;13:*.flv=38;5;13:*.gl=38;5;13:*.dl=38;5;13:*.xcf=38;5;13:*.xwd=38;5;13:*.yuv=38;5;13:*.cgm=38;5;13:*.emf=38;5;13:*.axv=38;5;13:*.anx=38;5;13:*.ogv=38;5;13:*.ogx=38;5;13:*.aac=38;5;45:*.au=38;5;45:*.flac=38;5;45:*.mid=38;5;45:*.midi=38;5;45:*.mka=38;5;45:*.mp3=38;5;45:*.mpc=38;5;45:*.ogg=38;5;45:*.ra=38;5;45:*.wav=38;5;45:*.axa=38;5;45:*.oga=38;5;45:*.spx=38;5;45:*.xspf=38;5;45:
-LD_LIBRARY_PATH=/nopt/slurm/current/lib:/nopt/slurm/current/lib:
-LMOD_sys=Linux
-CONDA_BACKUP_HOST=x86_64-conda_cos6-linux-gnu
-CONDA_BACKUP_F95=/nopt/nrel/apps/anaconda/5.3/bin/x86_64-conda_cos6-linux-gnu-f95
-NREL_CLUSTER=eagle
-CONDA_EXE=/nopt/nrel/apps/anaconda/5.3/bin/conda
-REQUESTS_CA_BUNDLE=/etc/ssl/certs/ca-bundle.crt
-SLURM_JOBID=6800229
-OSCAR_HOME=/opt/oscar
-CONDA_BACKUP_F90=/nopt/nrel/apps/anaconda/5.3/bin/x86_64-conda_cos6-linux-gnu-gfortran
-DEBUG_FORTRANFLAGS=-fopenmp -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -pipe -fopenmp -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-all -fno-plt -Og -g -Wall -Wextra -fcheck=all -fbacktrace -fimplicit-none -fvar-tracking-assignments -pipe
-__LMOD_REF_COUNT__LMFILES_=/nopt/nrel/apps/modules/default/modulefiles/conda/mini_py37_4.8.3:1
-CXXFLAGS=-fvisibility-inlines-hidden -std=c++17 -fmessage-length=0 -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -pipe
-SLURM_NTASKS=3
-LD_GOLD=/nopt/nrel/apps/anaconda/5.3/bin/x86_64-conda_cos6-linux-gnu-ld.gold
-STRINGS=/nopt/nrel/apps/anaconda/5.3/bin/x86_64-conda_cos6-linux-gnu-strings
-CPP=/nopt/nrel/apps/anaconda/5.3/bin/x86_64-conda_cos6-linux-gnu-cpp
-_CE_CONDA=
-CXXFILT=/nopt/nrel/apps/anaconda/5.3/bin/x86_64-conda_cos6-linux-gnu-c++filt
-SLURM_TASKS_PER_NODE=1(x3)
-PATH=/opt/clmgr/sbin:/opt/clmgr/bin:/opt/sgi/sbin:/opt/sgi/bin:/nopt/nrel/utils/bin:/nopt/slurm/current/bin:/nopt/nrel/apps/anaconda/5.3/bin:/nopt/nrel/apps/anaconda/mini_py37_4.8.3/bin:/nopt/nrel/apps/anaconda/5.3/bin:/nopt/nrel/apps/anaconda/5.3/condabin:/opt/clmgr/sbin:/opt/clmgr/bin:/opt/sgi/sbin:/opt/sgi/bin:/usr/lib64/qt-3.3/bin:/nopt/xalt/xalt/bin:/nopt/nrel/utils/bin:/nopt/slurm/current/bin:/usr/local/bin:/usr/bin:/usr/local/sbin:/usr/sbin:/opt/c3/bin:/opt/ibutils/bin:/sbin:/bin:/home/eskordil/.local/bin:/home/eskordil/bin:/sbin:/bin:/home/eskordil/.local/bin:/home/eskordil/bin
-MAIL=/var/spool/mail/eskordil
-_ModuleTable001_=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
-SLURM_WORKING_CLUSTER=eagle:10.148.7.234:6817:8960:101
-SLURM_CONF=/nopt/slurm/etc/slurm.conf
-SLURM_JOB_ID=6800229
-SLURM_CPUS_PER_TASK=36
-DEBUG_CXXFLAGS=-fvisibility-inlines-hidden -std=c++17 -fmessage-length=0 -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-all -fno-plt -Og -g -Wall -Wextra -fvar-tracking-assignments -pipe
-LD=/nopt/nrel/apps/anaconda/5.3/bin/x86_64-conda_cos6-linux-gnu-ld
-CONDA_PREFIX=/nopt/nrel/apps/anaconda/5.3
-SLURM_JOB_USER=eskordil
-PWD=/scratch/eskordil/git-repos/HPC/languages/python/openai_rllib/simple-example
-F90=/nopt/nrel/apps/anaconda/5.3/bin/x86_64-conda_cos6-linux-gnu-gfortran
-STRIP=/nopt/nrel/apps/anaconda/5.3/bin/x86_64-conda_cos6-linux-gnu-strip
-LOCAL_SCRATCH=/tmp/scratch
-XALT_TRANSMISSION_STYLE=syslog
-_LMFILES_=/nopt/nrel/apps/modules/default/modulefiles/conda/mini_py37_4.8.3
-ELFEDIT=/nopt/nrel/apps/anaconda/5.3/bin/x86_64-conda_cos6-linux-gnu-elfedit
-GCC_RANLIB=/nopt/nrel/apps/anaconda/5.3/bin/x86_64-conda_cos6-linux-gnu-gcc-ranlib
-LANG=en_US.UTF-8
-F95=/nopt/nrel/apps/anaconda/5.3/bin/x86_64-conda_cos6-linux-gnu-f95
-CONDA_PKGS_DIRS=~/.conda-pkgs
-MODULEPATH=/nopt/nrel/utils/modulefiles/Linux:/nopt/nrel/utils/modulefiles/Core:/nopt/nrel/apps/modules/default/modulefiles
-SLURM_JOB_UID=131442
-_ModuleTable_Sz_=2
-LOADEDMODULES=conda/mini_py37_4.8.3
-DEBUG_FFLAGS=-fopenmp -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -pipe -fopenmp -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-all -fno-plt -Og -g -Wall -Wextra -fcheck=all -fbacktrace -fimplicit-none -fvar-tracking-assignments -pipe
-SLURM_NODEID=0
-F77=/nopt/nrel/apps/anaconda/5.3/bin/x86_64-conda_cos6-linux-gnu-gfortran
-SLURM_SUBMIT_DIR=/lustre/eaglefs/scratch/eskordil/git-repos/HPC/languages/python/openai_rllib/simple-example
-SELINUX_LEVEL_REQUESTED=
-C3_RSH=ssh -oConnectTimeout=10 -oForwardX11=no
-SLURM_TASK_PID=215355
-SLURM_NPROCS=3
-LMOD_CMD=/nopt/nrel/utils/lmod/lmod/libexec/lmod
-SLURM_CPUS_ON_NODE=36
-CONDA_ENVS_PATH=~/.conda-envs
-CXX=/nopt/nrel/apps/anaconda/5.3/bin/x86_64-conda_cos6-linux-gnu-c++
-SLURM_PROCID=0
-ENVIRONMENT=BATCH
-HISTCONTROL=ignoredups
-_CE_M=
-OBJCOPY=/nopt/nrel/apps/anaconda/5.3/bin/x86_64-conda_cos6-linux-gnu-objcopy
-SLURM_JOB_NODELIST=r9i1n[11-12,32]
-HOME=/home/eskordil
-SHLVL=2
-SLURM_LOCALID=0
-LANGUAGE=en_US.UTF-8
-__LMOD_REF_COUNT_PATH=/nopt/nrel/apps/anaconda/mini_py37_4.8.3/bin:1;/nopt/nrel/apps/anaconda/5.3/bin:1;/nopt/nrel/apps/anaconda/5.3/condabin:1;/opt/clmgr/sbin:1;/opt/clmgr/bin:1;/opt/sgi/sbin:1;/opt/sgi/bin:1;/usr/lib64/qt-3.3/bin:1;/nopt/xalt/xalt/bin:1;/nopt/nrel/utils/bin:1;/nopt/slurm/current/bin:1;/usr/local/bin:1;/usr/bin:1;/usr/local/sbin:1;/usr/sbin:1;/opt/c3/bin:1;/opt/ibutils/bin:1;/sbin:1;/bin:1;/home/eskordil/.local/bin:1;/home/eskordil/bin:1
-FORTRANFLAGS=-fopenmp -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -pipe
-DEBUG_CPPFLAGS=-D_DEBUG -D_FORTIFY_SOURCE=2 -Og
-CFLAGS=-march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -pipe
-SLURM_JOB_GID=131442
-SLURM_JOB_CPUS_PER_NODE=36(x3)
-SLURM_CLUSTER_NAME=eagle
-XALT_SCALAR_SAMPLING=no
-_ModuleTable002_=cmUiLCIvbm9wdC9ucmVsL2FwcHMvbW9kdWxlcy9kZWZhdWx0L21vZHVsZWZpbGVzIix9LFsic3lzdGVtQmFzZU1QQVRIIl09Ii9ub3B0L25yZWwvdXRpbHMvbW9kdWxlZmlsZXMvTGludXg6L25vcHQvbnJlbC91dGlscy9tb2R1bGVmaWxlcy9Db3JlOi9ub3B0L25yZWwvYXBwcy9tb2R1bGVzL2RlZmF1bHQvbW9kdWxlZmlsZXMiLH0=
-FC=/nopt/nrel/apps/anaconda/5.3/bin/x86_64-conda_cos6-linux-gnu-gfortran
-_CONDA_PYTHON_SYSCONFIGDATA_NAME=_sysconfigdata_x86_64_conda_cos6_linux_gnu
-GCC=/nopt/nrel/apps/anaconda/5.3/bin/x86_64-conda_cos6-linux-gnu-gcc
-SLURM_GTIDS=0
-SLURM_SUBMIT_HOST=el1
-SLURM_JOB_PARTITION=short
-BASH_ENV=/nopt/nrel/utils/lmod/lmod/init/bash
-ADDR2LINE=/nopt/nrel/apps/anaconda/5.3/bin/x86_64-conda_cos6-linux-gnu-addr2line
-LOGNAME=eskordil
-CONDA_PYTHON_EXE=/nopt/nrel/apps/anaconda/5.3/bin/python
-QTLIB=/usr/lib64/qt-3.3/lib
-SLURM_JOB_ACCOUNT=rlldrd
-SSH_CONNECTION=10.10.141.83 49482 10.60.1.110 22
-XDG_DATA_DIRS=/home/eskordil/.local/share/flatpak/exports/share:/var/lib/flatpak/exports/share:/usr/local/share:/usr/share
-SLURM_JOB_NUM_NODES=3
-MODULESHOME=/nopt/nrel/utils/lmod/lmod
-LESSOPEN=||/usr/bin/lesspipe.sh %s
-LMOD_SETTARG_FULL_SUPPORT=no
-CONDA_DEFAULT_ENV=base
-DEBUG_CFLAGS=-march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-all -fno-plt -Og -g -Wall -Wextra -fvar-tracking-assignments -pipe
-RANLIB=/nopt/nrel/apps/anaconda/5.3/bin/x86_64-conda_cos6-linux-gnu-ranlib
-USE_PCM_DB=2
-CC=/nopt/nrel/apps/anaconda/5.3/bin/x86_64-conda_cos6-linux-gnu-cc
-XDG_RUNTIME_DIR=/run/user/131442
-READELF=/nopt/nrel/apps/anaconda/5.3/bin/x86_64-conda_cos6-linux-gnu-readelf
-LMOD_DIR=/nopt/nrel/utils/lmod/lmod/libexec
-__LMOD_REF_COUNT_MANPATH=/nopt/nrel/apps/anaconda/mini_py37_4.8.3/share/man:1;/nopt/nrel/utils/lmod/lmod/share/man:1;/nopt/slurm/current/share:1;/opt/c3/man:1;/opt/clmgr/man:1;/opt/sgi/share/man:1;/opt/clmgr/share/man:1;/opt/clmgr/lib/cm-cli/man:1
-GCC_AR=/nopt/nrel/apps/anaconda/5.3/bin/x86_64-conda_cos6-linux-gnu-gcc-ar
-OBJDUMP=/nopt/nrel/apps/anaconda/5.3/bin/x86_64-conda_cos6-linux-gnu-objdump
-GPROF=/nopt/nrel/apps/anaconda/5.3/bin/x86_64-conda_cos6-linux-gnu-gprof
-GXX=/nopt/nrel/apps/anaconda/5.3/bin/x86_64-conda_cos6-linux-gnu-g++
-FFLAGS=-fopenmp -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -pipe
-BASH_FUNC_module()=() {  eval $($LMOD_CMD bash "$@") && eval $(${LMOD_SETTARG_CMD:-:} -s sh)
-}
-BASH_FUNC_ml()=() {  eval $($LMOD_DIR/ml_cmd "$@")
-}
-_=/usr/bin/env
-worker_num=2
-total_cpus=72
-nodes: r9i1n11 r9i1n12 r9i1n32
-nodes_array: r9i1n11
-node1: r9i1n11
-ip_prefix: 10.148.8.236
-suffix: 6379
-ip_head: 10.148.8.236:6379
-redis_password: 395b6ccb-512c-4d3a-83a0-c6c930521556
-starting head at r9i1n11
-2021-05-07 15:55:08,334	INFO services.py:1269 -- View the Ray dashboard at http://127.0.0.1:8265
-starting workers
-i=1, node2=r9i1n12
-i=2, node2=r9i1n32
-calling simple_trainer.py
-Namespace(local_mode=False, name_env='CartPole-v0', num_cpus=72, num_gpus=0, redis_password='395b6ccb-512c-4d3a-83a0-c6c930521556', run='PPO')
-2021-05-07 15:55:34,084	INFO worker.py:641 -- Connecting to existing Ray cluster at address: 10.148.8.236:6379
-WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-Instructions for updating:
-non-resource variables are not supported in the long term
-== Status ==
-Memory usage on this node: 7.3/187.3 GiB
-Using FIFO scheduling algorithm.
-Resources requested: 0/108 CPUs, 0/0 GPUs, 0.0/378.15 GiB heap, 0.0/166.06 GiB objects
-Result logdir: /scratch/eskordil/ray_results/CartPole-v0
-Number of trials: 1/1 (1 PENDING)
-+-----------------------------+----------+-------+
-| Trial name                  | status   | loc   |
-|-----------------------------+----------+-------|
-| PPO_CartPole-v0_21a72_00000 | PENDING  |       |
-+-----------------------------+----------+-------+
-
-
-(pid=218685) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=218685) Instructions for updating:
-(pid=218685) non-resource variables are not supported in the long term
-(pid=218685) 2021-05-07 15:56:56,737	INFO trainer.py:669 -- Tip: set framework=tfe or the --eager flag to enable TensorFlow eager execution
-(pid=218685) 2021-05-07 15:56:56,737	INFO trainer.py:696 -- Current log_level is WARN. For more information, set 'log_level': 'INFO' / 'DEBUG' or use the -v and -vv flags.
-(pid=218706) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=218706) Instructions for updating:
-(pid=218706) non-resource variables are not supported in the long term
-(pid=218707) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=218707) Instructions for updating:
-(pid=218707) non-resource variables are not supported in the long term
-(pid=218713) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=218713) Instructions for updating:
-(pid=218713) non-resource variables are not supported in the long term
-(pid=218701) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=218701) Instructions for updating:
-(pid=218701) non-resource variables are not supported in the long term
-(pid=218700) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=218700) Instructions for updating:
-(pid=218700) non-resource variables are not supported in the long term
-(pid=218712) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=218712) Instructions for updating:
-(pid=218712) non-resource variables are not supported in the long term
-(pid=218687) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=218687) Instructions for updating:
-(pid=218687) non-resource variables are not supported in the long term
-(pid=218688) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=218688) Instructions for updating:
-(pid=218688) non-resource variables are not supported in the long term
-(pid=218689) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=218689) Instructions for updating:
-(pid=218689) non-resource variables are not supported in the long term
-(pid=218686) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=218686) Instructions for updating:
-(pid=218686) non-resource variables are not supported in the long term
-(pid=218934) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=218934) Instructions for updating:
-(pid=218934) non-resource variables are not supported in the long term
-(pid=218936) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=218936) Instructions for updating:
-(pid=218936) non-resource variables are not supported in the long term
-(pid=218940) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=218940) Instructions for updating:
-(pid=218940) non-resource variables are not supported in the long term
-(pid=218938) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=218938) Instructions for updating:
-(pid=218938) non-resource variables are not supported in the long term
-(pid=218937) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=218937) Instructions for updating:
-(pid=218937) non-resource variables are not supported in the long term
-(pid=218939) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=218939) Instructions for updating:
-(pid=218939) non-resource variables are not supported in the long term
-(pid=218946) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=218946) Instructions for updating:
-(pid=218946) non-resource variables are not supported in the long term
-(pid=218952) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=218952) Instructions for updating:
-(pid=218952) non-resource variables are not supported in the long term
-(pid=218965) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=218965) Instructions for updating:
-(pid=218965) non-resource variables are not supported in the long term
-(pid=218943) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=218943) Instructions for updating:
-(pid=218943) non-resource variables are not supported in the long term
-(pid=218935) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=218935) Instructions for updating:
-(pid=218935) non-resource variables are not supported in the long term
-(pid=218941) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=218941) Instructions for updating:
-(pid=218941) non-resource variables are not supported in the long term
-(pid=218959) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=218959) Instructions for updating:
-(pid=218959) non-resource variables are not supported in the long term
-(pid=218962) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=218962) Instructions for updating:
-(pid=218962) non-resource variables are not supported in the long term
-(pid=218949) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=218949) Instructions for updating:
-(pid=218949) non-resource variables are not supported in the long term
-(pid=218966) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=218966) Instructions for updating:
-(pid=218966) non-resource variables are not supported in the long term
-(pid=218985) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=218985) Instructions for updating:
-(pid=218985) non-resource variables are not supported in the long term
-(pid=218973) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=218973) Instructions for updating:
-(pid=218973) non-resource variables are not supported in the long term
-(pid=218967) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=218967) Instructions for updating:
-(pid=218967) non-resource variables are not supported in the long term
-(pid=218956) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=218956) Instructions for updating:
-(pid=218956) non-resource variables are not supported in the long term
-(pid=218982) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=218982) Instructions for updating:
-(pid=218982) non-resource variables are not supported in the long term
-(pid=218976) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=218976) Instructions for updating:
-(pid=218976) non-resource variables are not supported in the long term
-(pid=218970) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=218970) Instructions for updating:
-(pid=218970) non-resource variables are not supported in the long term
-(pid=218979) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=218979) Instructions for updating:
-(pid=218979) non-resource variables are not supported in the long term
-(pid=218997) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=218997) Instructions for updating:
-(pid=218997) non-resource variables are not supported in the long term
-(pid=218685) 2021-05-07 15:57:03,810	WARNING util.py:53 -- Install gputil for GPU system monitoring.
-(pid=214287, ip=10.148.8.241) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=214287, ip=10.148.8.241) Instructions for updating:
-(pid=214287, ip=10.148.8.241) non-resource variables are not supported in the long term
-(pid=214286, ip=10.148.8.241) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=214286, ip=10.148.8.241) Instructions for updating:
-(pid=214286, ip=10.148.8.241) non-resource variables are not supported in the long term
-(pid=214285, ip=10.148.8.241) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=214285, ip=10.148.8.241) Instructions for updating:
-(pid=214285, ip=10.148.8.241) non-resource variables are not supported in the long term
-(pid=214284, ip=10.148.8.241) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=214284, ip=10.148.8.241) Instructions for updating:
-(pid=214284, ip=10.148.8.241) non-resource variables are not supported in the long term
-(pid=214279, ip=10.148.8.241) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=214279, ip=10.148.8.241) Instructions for updating:
-(pid=214279, ip=10.148.8.241) non-resource variables are not supported in the long term
-(pid=214291, ip=10.148.8.241) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=214291, ip=10.148.8.241) Instructions for updating:
-(pid=214291, ip=10.148.8.241) non-resource variables are not supported in the long term
-(pid=214282, ip=10.148.8.241) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=214282, ip=10.148.8.241) Instructions for updating:
-(pid=214282, ip=10.148.8.241) non-resource variables are not supported in the long term
-(pid=214280, ip=10.148.8.241) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=214280, ip=10.148.8.241) Instructions for updating:
-(pid=214280, ip=10.148.8.241) non-resource variables are not supported in the long term
-(pid=214275, ip=10.148.8.241) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=214275, ip=10.148.8.241) Instructions for updating:
-(pid=214275, ip=10.148.8.241) non-resource variables are not supported in the long term
-(pid=214278, ip=10.148.8.241) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=214278, ip=10.148.8.241) Instructions for updating:
-(pid=214278, ip=10.148.8.241) non-resource variables are not supported in the long term
-(pid=214277, ip=10.148.8.241) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=214277, ip=10.148.8.241) Instructions for updating:
-(pid=214277, ip=10.148.8.241) non-resource variables are not supported in the long term
-(pid=214272, ip=10.148.8.241) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=214272, ip=10.148.8.241) Instructions for updating:
-(pid=214272, ip=10.148.8.241) non-resource variables are not supported in the long term
-(pid=214281, ip=10.148.8.241) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=214281, ip=10.148.8.241) Instructions for updating:
-(pid=214281, ip=10.148.8.241) non-resource variables are not supported in the long term
-(pid=214273, ip=10.148.8.241) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=214273, ip=10.148.8.241) Instructions for updating:
-(pid=214273, ip=10.148.8.241) non-resource variables are not supported in the long term
-(pid=214274, ip=10.148.8.241) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=214274, ip=10.148.8.241) Instructions for updating:
-(pid=214274, ip=10.148.8.241) non-resource variables are not supported in the long term
-(pid=214276, ip=10.148.8.241) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=214276, ip=10.148.8.241) Instructions for updating:
-(pid=214276, ip=10.148.8.241) non-resource variables are not supported in the long term
-(pid=214283, ip=10.148.8.241) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=214283, ip=10.148.8.241) Instructions for updating:
-(pid=214283, ip=10.148.8.241) non-resource variables are not supported in the long term
-(pid=214323, ip=10.148.8.241) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=214323, ip=10.148.8.241) Instructions for updating:
-(pid=214323, ip=10.148.8.241) non-resource variables are not supported in the long term
-(pid=214318, ip=10.148.8.241) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=214318, ip=10.148.8.241) Instructions for updating:
-(pid=214318, ip=10.148.8.241) non-resource variables are not supported in the long term
-(pid=214288, ip=10.148.8.241) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=214288, ip=10.148.8.241) Instructions for updating:
-(pid=214288, ip=10.148.8.241) non-resource variables are not supported in the long term
-(pid=214327, ip=10.148.8.241) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=214327, ip=10.148.8.241) Instructions for updating:
-(pid=214327, ip=10.148.8.241) non-resource variables are not supported in the long term
-(pid=214290, ip=10.148.8.241) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=214290, ip=10.148.8.241) Instructions for updating:
-(pid=214290, ip=10.148.8.241) non-resource variables are not supported in the long term
-(pid=214297, ip=10.148.8.241) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=214297, ip=10.148.8.241) Instructions for updating:
-(pid=214297, ip=10.148.8.241) non-resource variables are not supported in the long term
-(pid=214328, ip=10.148.8.241) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=214328, ip=10.148.8.241) Instructions for updating:
-(pid=214328, ip=10.148.8.241) non-resource variables are not supported in the long term
-(pid=214298, ip=10.148.8.241) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=214298, ip=10.148.8.241) Instructions for updating:
-(pid=214298, ip=10.148.8.241) non-resource variables are not supported in the long term
-(pid=214314, ip=10.148.8.241) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=214314, ip=10.148.8.241) Instructions for updating:
-(pid=214314, ip=10.148.8.241) non-resource variables are not supported in the long term
-(pid=214319, ip=10.148.8.241) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=214319, ip=10.148.8.241) Instructions for updating:
-(pid=214319, ip=10.148.8.241) non-resource variables are not supported in the long term
-(pid=214292, ip=10.148.8.241) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=214292, ip=10.148.8.241) Instructions for updating:
-(pid=214292, ip=10.148.8.241) non-resource variables are not supported in the long term
-(pid=214310, ip=10.148.8.241) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=214310, ip=10.148.8.241) Instructions for updating:
-(pid=214310, ip=10.148.8.241) non-resource variables are not supported in the long term
-(pid=214302, ip=10.148.8.241) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=214302, ip=10.148.8.241) Instructions for updating:
-(pid=214302, ip=10.148.8.241) non-resource variables are not supported in the long term
-(pid=214295, ip=10.148.8.241) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=214295, ip=10.148.8.241) Instructions for updating:
-(pid=214295, ip=10.148.8.241) non-resource variables are not supported in the long term
-(pid=214300, ip=10.148.8.241) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=214300, ip=10.148.8.241) Instructions for updating:
-(pid=214300, ip=10.148.8.241) non-resource variables are not supported in the long term
-(pid=214304, ip=10.148.8.241) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=214304, ip=10.148.8.241) Instructions for updating:
-(pid=214304, ip=10.148.8.241) non-resource variables are not supported in the long term
-(pid=214315, ip=10.148.8.241) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=214315, ip=10.148.8.241) Instructions for updating:
-(pid=214315, ip=10.148.8.241) non-resource variables are not supported in the long term
-(pid=214303, ip=10.148.8.241) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=214303, ip=10.148.8.241) Instructions for updating:
-(pid=214303, ip=10.148.8.241) non-resource variables are not supported in the long term
-(pid=214289, ip=10.148.8.241) WARNING:tensorflow:From /projects/rlldrd/eskordil/envs/env_example/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
-(pid=214289, ip=10.148.8.241) Instructions for updating:
-(pid=214289, ip=10.148.8.241) non-resource variables are not supported in the long term
-(pid=218685) 2021-05-07 15:57:09,982	WARNING deprecation.py:34 -- DeprecationWarning: `SampleBatch.data[..]` has been deprecated. Use `SampleBatch[..]` instead. This will raise an error in the future!
-Result for PPO_CartPole-v0_21a72_00000:
-  agent_timesteps_total: 14200
-  custom_metrics: {}
-  date: 2021-05-07_15-57-15
-  done: false
-  episode_len_mean: 21.424437299035368
-  episode_media: {}
-  episode_reward_max: 81.0
-  episode_reward_mean: 21.424437299035368
-  episode_reward_min: 8.0
-  episodes_this_iter: 622
-  episodes_total: 622
-  experiment_id: 11a144229f2c421fbea9e1bcd7956598
-  hostname: r9i1n11
-  info:
-    learner:
-      default_policy:
-        learner_stats:
-          cur_kl_coeff: 0.20000000298023224
-          cur_lr: 4.999999873689376e-05
-          entropy: 0.6593455672264099
-          entropy_coeff: 0.0
-          kl: 0.03552492707967758
-          model: {}
-          policy_loss: -0.048600681126117706
-          total_loss: 81.5534439086914
-          vf_explained_var: 0.27602478861808777
-          vf_loss: 81.59493255615234
-    num_agent_steps_sampled: 14200
-    num_steps_sampled: 14200
-    num_steps_trained: 14200
-  iterations_since_restore: 1
-  node_ip: 10.148.8.236
-  num_healthy_workers: 71
-  off_policy_estimator: {}
-  perf:
-    cpu_util_percent: 9.783333333333335
-    ram_util_percent: 9.0
-  pid: 218685
-  policy_reward_max: {}
-  policy_reward_mean: {}
-  policy_reward_min: {}
-  sampler_perf:
-    mean_action_processing_ms: 0.044887798294863376
-    mean_env_render_ms: 0.0
-    mean_env_wait_ms: 0.05714855505926562
-    mean_inference_ms: 0.7704948727150243
-    mean_raw_obs_processing_ms: 0.0727470746734268
-  time_since_restore: 12.102100849151611
-  time_this_iter_s: 12.102100849151611
-  time_total_s: 12.102100849151611
-  timers:
-    learn_throughput: 2412.026
-    learn_time_ms: 5887.167
-    sample_throughput: 2308.055
-    sample_time_ms: 6152.366
-    update_time_ms: 6.489
-  timestamp: 1620424635
-  timesteps_since_restore: 0
-  timesteps_total: 14200
-  training_iteration: 1
-  trial_id: 21a72_00000
-  
-== Status ==
-Memory usage on this node: 16.8/187.3 GiB
-Using FIFO scheduling algorithm.
-Resources requested: 72.0/108 CPUs, 0/0 GPUs, 0.0/378.15 GiB heap, 0.0/166.06 GiB objects
-Result logdir: /scratch/eskordil/ray_results/CartPole-v0
-Number of trials: 1/1 (1 RUNNING)
-+-----------------------------+----------+---------------------+--------+------------------+-------+----------+----------------------+----------------------+--------------------+
-| Trial name                  | status   | loc                 |   iter |   total time (s) |    ts |   reward |   episode_reward_max |   episode_reward_min |   episode_len_mean |
-|-----------------------------+----------+---------------------+--------+------------------+-------+----------+----------------------+----------------------+--------------------|
-| PPO_CartPole-v0_21a72_00000 | RUNNING  | 10.148.8.236:218685 |      1 |          12.1021 | 14200 |  21.4244 |                   81 |                    8 |            21.4244 |
-+-----------------------------+----------+---------------------+--------+------------------+-------+----------+----------------------+----------------------+--------------------+
-
-
-Result for PPO_CartPole-v0_21a72_00000:
-  agent_timesteps_total: 28400
-  custom_metrics: {}
-  date: 2021-05-07_15-57-21
-  done: false
-  episode_len_mean: 43.28125
-  episode_media: {}
-  episode_reward_max: 137.0
-  episode_reward_mean: 43.28125
-  episode_reward_min: 8.0
-  episodes_this_iter: 288
-  episodes_total: 910
-  experiment_id: 11a144229f2c421fbea9e1bcd7956598
-  hostname: r9i1n11
-  info:
-    learner:
-      default_policy:
-        learner_stats:
-          cur_kl_coeff: 0.30000001192092896
-          cur_lr: 4.999999873689376e-05
-          entropy: 0.5989530682563782
-          entropy_coeff: 0.0
-          kl: 0.019896699115633965
-          model: {}
-          policy_loss: -0.0315469391644001
-          total_loss: 278.09149169921875
-          vf_explained_var: 0.18729764223098755
-          vf_loss: 278.1170959472656
-    num_agent_steps_sampled: 28400
-    num_steps_sampled: 28400
-    num_steps_trained: 28400
-  iterations_since_restore: 2
-  node_ip: 10.148.8.236
-  num_healthy_workers: 71
-  off_policy_estimator: {}
-  perf:
-    cpu_util_percent: 14.9375
-    ram_util_percent: 9.0
-  pid: 218685
-  policy_reward_max: {}
-  policy_reward_mean: {}
-  policy_reward_min: {}
-  sampler_perf:
-    mean_action_processing_ms: 0.044350019697668684
-    mean_env_render_ms: 0.0
-    mean_env_wait_ms: 0.057039467060985456
-    mean_inference_ms: 0.7036283950820198
-    mean_raw_obs_processing_ms: 0.06796542454968202
-  time_since_restore: 17.810351371765137
-  time_this_iter_s: 5.708250522613525
-  time_total_s: 17.810351371765137
-  timers:
-    learn_throughput: 2503.637
-    learn_time_ms: 5671.749
-    sample_throughput: 4483.587
-    sample_time_ms: 3167.107
-    update_time_ms: 6.341
-  timestamp: 1620424641
-  timesteps_since_restore: 0
-  timesteps_total: 28400
-  training_iteration: 2
-  trial_id: 21a72_00000
-  
-== Status ==
-Memory usage on this node: 16.9/187.3 GiB
-Using FIFO scheduling algorithm.
-Resources requested: 72.0/108 CPUs, 0/0 GPUs, 0.0/378.15 GiB heap, 0.0/166.06 GiB objects
-Result logdir: /scratch/eskordil/ray_results/CartPole-v0
-Number of trials: 1/1 (1 RUNNING)
-+-----------------------------+----------+---------------------+--------+------------------+-------+----------+----------------------+----------------------+--------------------+
-| Trial name                  | status   | loc                 |   iter |   total time (s) |    ts |   reward |   episode_reward_max |   episode_reward_min |   episode_len_mean |
-|-----------------------------+----------+---------------------+--------+------------------+-------+----------+----------------------+----------------------+--------------------|
-| PPO_CartPole-v0_21a72_00000 | RUNNING  | 10.148.8.236:218685 |      2 |          17.8104 | 28400 |  43.2812 |                  137 |                    8 |            43.2812 |
-+-----------------------------+----------+---------------------+--------+------------------+-------+----------+----------------------+----------------------+--------------------+
-
-
-Result for PPO_CartPole-v0_21a72_00000:
-  agent_timesteps_total: 42600
-  custom_metrics: {}
-  date: 2021-05-07_15-57-27
-  done: false
-  episode_len_mean: 93.73643410852713
-  episode_media: {}
-  episode_reward_max: 200.0
-  episode_reward_mean: 93.73643410852713
-  episode_reward_min: 14.0
-  episodes_this_iter: 129
-  episodes_total: 1039
-  experiment_id: 11a144229f2c421fbea9e1bcd7956598
-  hostname: r9i1n11
-  info:
-    learner:
-      default_policy:
-        learner_stats:
-          cur_kl_coeff: 0.30000001192092896
-          cur_lr: 4.999999873689376e-05
-          entropy: 0.5623010993003845
-          entropy_coeff: 0.0
-          kl: 0.010707436129450798
-          model: {}
-          policy_loss: -0.014771048910915852
-          total_loss: 508.09857177734375
-          vf_explained_var: 0.2104642689228058
-          vf_loss: 508.1101989746094
-    num_agent_steps_sampled: 42600
-    num_steps_sampled: 42600
-    num_steps_trained: 42600
-  iterations_since_restore: 3
-  node_ip: 10.148.8.236
-  num_healthy_workers: 71
-  off_policy_estimator: {}
-  perf:
-    cpu_util_percent: 14.549999999999999
-    ram_util_percent: 9.0
-  pid: 218685
-  policy_reward_max: {}
-  policy_reward_mean: {}
-  policy_reward_min: {}
-  sampler_perf:
-    mean_action_processing_ms: 0.04410248295753331
-    mean_env_render_ms: 0.0
-    mean_env_wait_ms: 0.05670065180455692
-    mean_inference_ms: 0.6789937564461294
-    mean_raw_obs_processing_ms: 0.06467116797165183
-  time_since_restore: 23.54135799407959
-  time_this_iter_s: 5.731006622314453
-  time_total_s: 23.54135799407959
-  timers:
-    learn_throughput: 2531.248
-    learn_time_ms: 5609.88
-    sample_throughput: 6541.274
-    sample_time_ms: 2170.831
-    update_time_ms: 6.265
-  timestamp: 1620424647
-  timesteps_since_restore: 0
-  timesteps_total: 42600
-  training_iteration: 3
-  trial_id: 21a72_00000
-  
-== Status ==
-Memory usage on this node: 16.9/187.3 GiB
-Using FIFO scheduling algorithm.
-Resources requested: 72.0/108 CPUs, 0/0 GPUs, 0.0/378.15 GiB heap, 0.0/166.06 GiB objects
-Result logdir: /scratch/eskordil/ray_results/CartPole-v0
-Number of trials: 1/1 (1 RUNNING)
-+-----------------------------+----------+---------------------+--------+------------------+-------+----------+----------------------+----------------------+--------------------+
-| Trial name                  | status   | loc                 |   iter |   total time (s) |    ts |   reward |   episode_reward_max |   episode_reward_min |   episode_len_mean |
-|-----------------------------+----------+---------------------+--------+------------------+-------+----------+----------------------+----------------------+--------------------|
-| PPO_CartPole-v0_21a72_00000 | RUNNING  | 10.148.8.236:218685 |      3 |          23.5414 | 42600 |  93.7364 |                  200 |                   14 |            93.7364 |
-+-----------------------------+----------+---------------------+--------+------------------+-------+----------+----------------------+----------------------+--------------------+
-
-
-Result for PPO_CartPole-v0_21a72_00000:
-  agent_timesteps_total: 56800
-  custom_metrics: {}
-  date: 2021-05-07_15-57-33
-  done: false
-  episode_len_mean: 143.28
-  episode_media: {}
-  episode_reward_max: 200.0
-  episode_reward_mean: 143.28
-  episode_reward_min: 12.0
-  episodes_this_iter: 85
-  episodes_total: 1124
-  experiment_id: 11a144229f2c421fbea9e1bcd7956598
-  hostname: r9i1n11
-  info:
-    learner:
-      default_policy:
-        learner_stats:
-          cur_kl_coeff: 0.30000001192092896
-          cur_lr: 4.999999873689376e-05
-          entropy: 0.5589228272438049
-          entropy_coeff: 0.0
-          kl: 0.002630604198202491
-          model: {}
-          policy_loss: -0.003993053454905748
-          total_loss: 421.9928894042969
-          vf_explained_var: 0.3999801576137543
-          vf_loss: 421.9961853027344
-    num_agent_steps_sampled: 56800
-    num_steps_sampled: 56800
-    num_steps_trained: 56800
-  iterations_since_restore: 4
-  node_ip: 10.148.8.236
-  num_healthy_workers: 71
-  off_policy_estimator: {}
-  perf:
-    cpu_util_percent: 14.625
-    ram_util_percent: 9.0
-  pid: 218685
-  policy_reward_max: {}
-  policy_reward_mean: {}
-  policy_reward_min: {}
-  sampler_perf:
-    mean_action_processing_ms: 0.04416523166330421
-    mean_env_render_ms: 0.0
-    mean_env_wait_ms: 0.056963442878503086
-    mean_inference_ms: 0.6734566083567803
-    mean_raw_obs_processing_ms: 0.06334069564616224
-  time_since_restore: 29.208738803863525
-  time_this_iter_s: 5.6673808097839355
-  time_total_s: 29.208738803863525
-  timers:
-    learn_throughput: 2553.095
-    learn_time_ms: 5561.876
-    sample_throughput: 8489.864
-    sample_time_ms: 1672.583
-    update_time_ms: 6.265
-  timestamp: 1620424653
-  timesteps_since_restore: 0
-  timesteps_total: 56800
-  training_iteration: 4
-  trial_id: 21a72_00000
-  
-== Status ==
-Memory usage on this node: 16.9/187.3 GiB
-Using FIFO scheduling algorithm.
-Resources requested: 72.0/108 CPUs, 0/0 GPUs, 0.0/378.15 GiB heap, 0.0/166.06 GiB objects
-Result logdir: /scratch/eskordil/ray_results/CartPole-v0
-Number of trials: 1/1 (1 RUNNING)
-+-----------------------------+----------+---------------------+--------+------------------+-------+----------+----------------------+----------------------+--------------------+
-| Trial name                  | status   | loc                 |   iter |   total time (s) |    ts |   reward |   episode_reward_max |   episode_reward_min |   episode_len_mean |
-|-----------------------------+----------+---------------------+--------+------------------+-------+----------+----------------------+----------------------+--------------------|
-| PPO_CartPole-v0_21a72_00000 | RUNNING  | 10.148.8.236:218685 |      4 |          29.2087 | 56800 |   143.28 |                  200 |                   12 |             143.28 |
-+-----------------------------+----------+---------------------+--------+------------------+-------+----------+----------------------+----------------------+--------------------+
-
-
-Result for PPO_CartPole-v0_21a72_00000:
-  agent_timesteps_total: 71000
-  custom_metrics: {}
-  date: 2021-05-07_15-57-38
-  done: false
-  episode_len_mean: 169.84
-  episode_media: {}
-  episode_reward_max: 200.0
-  episode_reward_mean: 169.84
-  episode_reward_min: 12.0
-  episodes_this_iter: 74
-  episodes_total: 1198
-  experiment_id: 11a144229f2c421fbea9e1bcd7956598
-  hostname: r9i1n11
-  info:
-    learner:
-      default_policy:
-        learner_stats:
-          cur_kl_coeff: 0.15000000596046448
-          cur_lr: 4.999999873689376e-05
-          entropy: 0.52360999584198
-          entropy_coeff: 0.0
-          kl: 0.008638556115329266
-          model: {}
-          policy_loss: -0.00537338200956583
-          total_loss: 331.78875732421875
-          vf_explained_var: 0.4499698281288147
-          vf_loss: 331.79278564453125
-    num_agent_steps_sampled: 71000
-    num_steps_sampled: 71000
-    num_steps_trained: 71000
-  iterations_since_restore: 5
-  node_ip: 10.148.8.236
-  num_healthy_workers: 71
-  off_policy_estimator: {}
-  perf:
-    cpu_util_percent: 14.988888888888889
-    ram_util_percent: 9.0
-  pid: 218685
-  policy_reward_max: {}
-  policy_reward_mean: {}
-  policy_reward_min: {}
-  sampler_perf:
-    mean_action_processing_ms: 0.04398922254029637
-    mean_env_render_ms: 0.0
-    mean_env_wait_ms: 0.05685412335334053
-    mean_inference_ms: 0.6662084127517345
-    mean_raw_obs_processing_ms: 0.062235036020993624
-  time_since_restore: 34.978689432144165
-  time_this_iter_s: 5.76995062828064
-  time_total_s: 34.978689432144165
-  timers:
-    learn_throughput: 2557.542
-    learn_time_ms: 5552.206
-    sample_throughput: 10325.151
-    sample_time_ms: 1375.283
-    update_time_ms: 6.47
-  timestamp: 1620424658
-  timesteps_since_restore: 0
-  timesteps_total: 71000
-  training_iteration: 5
-  trial_id: 21a72_00000
-  
-== Status ==
-Memory usage on this node: 16.9/187.3 GiB
-Using FIFO scheduling algorithm.
-Resources requested: 72.0/108 CPUs, 0/0 GPUs, 0.0/378.15 GiB heap, 0.0/166.06 GiB objects
-Result logdir: /scratch/eskordil/ray_results/CartPole-v0
-Number of trials: 1/1 (1 RUNNING)
-+-----------------------------+----------+---------------------+--------+------------------+-------+----------+----------------------+----------------------+--------------------+
-| Trial name                  | status   | loc                 |   iter |   total time (s) |    ts |   reward |   episode_reward_max |   episode_reward_min |   episode_len_mean |
-|-----------------------------+----------+---------------------+--------+------------------+-------+----------+----------------------+----------------------+--------------------|
-| PPO_CartPole-v0_21a72_00000 | RUNNING  | 10.148.8.236:218685 |      5 |          34.9787 | 71000 |   169.84 |                  200 |                   12 |             169.84 |
-+-----------------------------+----------+---------------------+--------+------------------+-------+----------+----------------------+----------------------+--------------------+
-
-
-Result for PPO_CartPole-v0_21a72_00000:
-  agent_timesteps_total: 85200
-  custom_metrics: {}
-  date: 2021-05-07_15-57-44
-  done: false
-  episode_len_mean: 191.85
-  episode_media: {}
-  episode_reward_max: 200.0
-  episode_reward_mean: 191.85
-  episode_reward_min: 84.0
-  episodes_this_iter: 72
-  episodes_total: 1270
-  experiment_id: 11a144229f2c421fbea9e1bcd7956598
-  hostname: r9i1n11
-  info:
-    learner:
-      default_policy:
-        learner_stats:
-          cur_kl_coeff: 0.15000000596046448
-          cur_lr: 4.999999873689376e-05
-          entropy: 0.5126173496246338
-          entropy_coeff: 0.0
-          kl: 0.004817360080778599
-          model: {}
-          policy_loss: -0.0022779516875743866
-          total_loss: 278.4949951171875
-          vf_explained_var: 0.5082927942276001
-          vf_loss: 278.49652099609375
-    num_agent_steps_sampled: 85200
-    num_steps_sampled: 85200
-    num_steps_trained: 85200
-  iterations_since_restore: 6
-  node_ip: 10.148.8.236
-  num_healthy_workers: 71
-  off_policy_estimator: {}
-  perf:
-    cpu_util_percent: 14.225
-    ram_util_percent: 9.0
-  pid: 218685
-  policy_reward_max: {}
-  policy_reward_mean: {}
-  policy_reward_min: {}
-  sampler_perf:
-    mean_action_processing_ms: 0.0439867391790659
-    mean_env_render_ms: 0.0
-    mean_env_wait_ms: 0.0568818033406756
-    mean_inference_ms: 0.6619861009709648
-    mean_raw_obs_processing_ms: 0.06138879261379005
-  time_since_restore: 40.72318434715271
-  time_this_iter_s: 5.744494915008545
-  time_total_s: 40.72318434715271
-  timers:
-    learn_throughput: 2561.424
-    learn_time_ms: 5543.791
-    sample_throughput: 12075.39
-    sample_time_ms: 1175.945
-    update_time_ms: 6.472
-  timestamp: 1620424664
-  timesteps_since_restore: 0
-  timesteps_total: 85200
-  training_iteration: 6
-  trial_id: 21a72_00000
-  
-== Status ==
-Memory usage on this node: 16.9/187.3 GiB
-Using FIFO scheduling algorithm.
-Resources requested: 72.0/108 CPUs, 0/0 GPUs, 0.0/378.15 GiB heap, 0.0/166.06 GiB objects
-Result logdir: /scratch/eskordil/ray_results/CartPole-v0
-Number of trials: 1/1 (1 RUNNING)
-+-----------------------------+----------+---------------------+--------+------------------+-------+----------+----------------------+----------------------+--------------------+
-| Trial name                  | status   | loc                 |   iter |   total time (s) |    ts |   reward |   episode_reward_max |   episode_reward_min |   episode_len_mean |
-|-----------------------------+----------+---------------------+--------+------------------+-------+----------+----------------------+----------------------+--------------------|
-| PPO_CartPole-v0_21a72_00000 | RUNNING  | 10.148.8.236:218685 |      6 |          40.7232 | 85200 |   191.85 |                  200 |                   84 |             191.85 |
-+-----------------------------+----------+---------------------+--------+------------------+-------+----------+----------------------+----------------------+--------------------+
-
-
-Result for PPO_CartPole-v0_21a72_00000:
-  agent_timesteps_total: 99400
-  custom_metrics: {}
-  date: 2021-05-07_15-57-50
-  done: false
-  episode_len_mean: 199.0
-  episode_media: {}
-  episode_reward_max: 200.0
-  episode_reward_mean: 199.0
-  episode_reward_min: 157.0
-  episodes_this_iter: 71
-  episodes_total: 1341
-  experiment_id: 11a144229f2c421fbea9e1bcd7956598
-  hostname: r9i1n11
-  info:
-    learner:
-      default_policy:
-        learner_stats:
-          cur_kl_coeff: 0.07500000298023224
-          cur_lr: 4.999999873689376e-05
-          entropy: 0.5216121077537537
-          entropy_coeff: 0.0
-          kl: 0.006858043372631073
-          model: {}
-          policy_loss: -0.00366325955837965
-          total_loss: 304.49566650390625
-          vf_explained_var: 0.47628024220466614
-          vf_loss: 304.4988098144531
-    num_agent_steps_sampled: 99400
-    num_steps_sampled: 99400
-    num_steps_trained: 99400
-  iterations_since_restore: 7
-  node_ip: 10.148.8.236
-  num_healthy_workers: 71
-  off_policy_estimator: {}
-  perf:
-    cpu_util_percent: 14.925
-    ram_util_percent: 9.0
-  pid: 218685
-  policy_reward_max: {}
-  policy_reward_mean: {}
-  policy_reward_min: {}
-  sampler_perf:
-    mean_action_processing_ms: 0.04397799687634829
-    mean_env_render_ms: 0.0
-    mean_env_wait_ms: 0.05686447762557306
-    mean_inference_ms: 0.6585671898683328
-    mean_raw_obs_processing_ms: 0.06080410167053978
-  time_since_restore: 46.46852254867554
-  time_this_iter_s: 5.745338201522827
-  time_total_s: 46.46852254867554
-  timers:
-    learn_throughput: 2564.24
-    learn_time_ms: 5537.702
-    sample_throughput: 13744.375
-    sample_time_ms: 1033.15
-    update_time_ms: 6.539
-  timestamp: 1620424670
-  timesteps_since_restore: 0
-  timesteps_total: 99400
-  training_iteration: 7
-  trial_id: 21a72_00000
-  
-== Status ==
-Memory usage on this node: 16.9/187.3 GiB
-Using FIFO scheduling algorithm.
-Resources requested: 72.0/108 CPUs, 0/0 GPUs, 0.0/378.15 GiB heap, 0.0/166.06 GiB objects
-Result logdir: /scratch/eskordil/ray_results/CartPole-v0
-Number of trials: 1/1 (1 RUNNING)
-+-----------------------------+----------+---------------------+--------+------------------+-------+----------+----------------------+----------------------+--------------------+
-| Trial name                  | status   | loc                 |   iter |   total time (s) |    ts |   reward |   episode_reward_max |   episode_reward_min |   episode_len_mean |
-|-----------------------------+----------+---------------------+--------+------------------+-------+----------+----------------------+----------------------+--------------------|
-| PPO_CartPole-v0_21a72_00000 | RUNNING  | 10.148.8.236:218685 |      7 |          46.4685 | 99400 |      199 |                  200 |                  157 |                199 |
-+-----------------------------+----------+---------------------+--------+------------------+-------+----------+----------------------+----------------------+--------------------+
-
-
-Result for PPO_CartPole-v0_21a72_00000:
-  agent_timesteps_total: 113600
-  custom_metrics: {}
-  date: 2021-05-07_15-57-56
-  done: false
-  episode_len_mean: 200.0
-  episode_media: {}
-  episode_reward_max: 200.0
-  episode_reward_mean: 200.0
-  episode_reward_min: 200.0
-  episodes_this_iter: 71
-  episodes_total: 1412
-  experiment_id: 11a144229f2c421fbea9e1bcd7956598
-  hostname: r9i1n11
-  info:
-    learner:
-      default_policy:
-        learner_stats:
-          cur_kl_coeff: 0.07500000298023224
-          cur_lr: 4.999999873689376e-05
-          entropy: 0.4980354309082031
-          entropy_coeff: 0.0
-          kl: 0.0029554071370512247
-          model: {}
-          policy_loss: -0.001304695033468306
-          total_loss: 359.4576721191406
-          vf_explained_var: 0.4212561845779419
-          vf_loss: 359.45880126953125
-    num_agent_steps_sampled: 113600
-    num_steps_sampled: 113600
-    num_steps_trained: 113600
-  iterations_since_restore: 8
-  node_ip: 10.148.8.236
-  num_healthy_workers: 71
-  off_policy_estimator: {}
-  perf:
-    cpu_util_percent: 14.9
-    ram_util_percent: 9.0
-  pid: 218685
-  policy_reward_max: {}
-  policy_reward_mean: {}
-  policy_reward_min: {}
-  sampler_perf:
-    mean_action_processing_ms: 0.043968966772636744
-    mean_env_render_ms: 0.0
-    mean_env_wait_ms: 0.05687874818913689
-    mean_inference_ms: 0.6562300588519817
-    mean_raw_obs_processing_ms: 0.06036881110893038
-  time_since_restore: 52.21696376800537
-  time_this_iter_s: 5.748441219329834
-  time_total_s: 52.21696376800537
-  timers:
-    learn_throughput: 2566.552
-    learn_time_ms: 5532.715
-    sample_throughput: 15327.851
-    sample_time_ms: 926.418
-    update_time_ms: 6.53
-  timestamp: 1620424676
-  timesteps_since_restore: 0
-  timesteps_total: 113600
-  training_iteration: 8
-  trial_id: 21a72_00000
-  
-== Status ==
-Memory usage on this node: 16.9/187.3 GiB
-Using FIFO scheduling algorithm.
-Resources requested: 72.0/108 CPUs, 0/0 GPUs, 0.0/378.15 GiB heap, 0.0/166.06 GiB objects
-Result logdir: /scratch/eskordil/ray_results/CartPole-v0
-Number of trials: 1/1 (1 RUNNING)
-+-----------------------------+----------+---------------------+--------+------------------+--------+----------+----------------------+----------------------+--------------------+
-| Trial name                  | status   | loc                 |   iter |   total time (s) |     ts |   reward |   episode_reward_max |   episode_reward_min |   episode_len_mean |
-|-----------------------------+----------+---------------------+--------+------------------+--------+----------+----------------------+----------------------+--------------------|
-| PPO_CartPole-v0_21a72_00000 | RUNNING  | 10.148.8.236:218685 |      8 |           52.217 | 113600 |      200 |                  200 |                  200 |                200 |
-+-----------------------------+----------+---------------------+--------+------------------+--------+----------+----------------------+----------------------+--------------------+
-
-
-Result for PPO_CartPole-v0_21a72_00000:
-  agent_timesteps_total: 127800
-  custom_metrics: {}
-  date: 2021-05-07_15-58-01
-  done: false
-  episode_len_mean: 200.0
-  episode_media: {}
-  episode_reward_max: 200.0
-  episode_reward_mean: 200.0
-  episode_reward_min: 200.0
-  episodes_this_iter: 71
-  episodes_total: 1483
-  experiment_id: 11a144229f2c421fbea9e1bcd7956598
-  hostname: r9i1n11
-  info:
-    learner:
-      default_policy:
-        learner_stats:
-          cur_kl_coeff: 0.03750000149011612
-          cur_lr: 4.999999873689376e-05
-          entropy: 0.4811287522315979
-          entropy_coeff: 0.0
-          kl: 0.004738766234368086
-          model: {}
-          policy_loss: -0.0010756243718788028
-          total_loss: 307.0951232910156
-          vf_explained_var: 0.48555827140808105
-          vf_loss: 307.0959777832031
-    num_agent_steps_sampled: 127800
-    num_steps_sampled: 127800
-    num_steps_trained: 127800
-  iterations_since_restore: 9
-  node_ip: 10.148.8.236
-  num_healthy_workers: 71
-  off_policy_estimator: {}
-  perf:
-    cpu_util_percent: 14.875
-    ram_util_percent: 9.0
-  pid: 218685
-  policy_reward_max: {}
-  policy_reward_mean: {}
-  policy_reward_min: {}
-  sampler_perf:
-    mean_action_processing_ms: 0.04394254691407144
-    mean_env_render_ms: 0.0
-    mean_env_wait_ms: 0.056880782745206915
-    mean_inference_ms: 0.6541343121951186
-    mean_raw_obs_processing_ms: 0.060043924322795074
-  time_since_restore: 57.968069076538086
-  time_this_iter_s: 5.751105308532715
-  time_total_s: 57.968069076538086
-  timers:
-    learn_throughput: 2569.023
-    learn_time_ms: 5527.392
-    sample_throughput: 16840.396
-    sample_time_ms: 843.211
-    update_time_ms: 6.492
-  timestamp: 1620424681
-  timesteps_since_restore: 0
-  timesteps_total: 127800
-  training_iteration: 9
-  trial_id: 21a72_00000
-  
-== Status ==
-Memory usage on this node: 16.9/187.3 GiB
-Using FIFO scheduling algorithm.
-Resources requested: 72.0/108 CPUs, 0/0 GPUs, 0.0/378.15 GiB heap, 0.0/166.06 GiB objects
-Result logdir: /scratch/eskordil/ray_results/CartPole-v0
-Number of trials: 1/1 (1 RUNNING)
-+-----------------------------+----------+---------------------+--------+------------------+--------+----------+----------------------+----------------------+--------------------+
-| Trial name                  | status   | loc                 |   iter |   total time (s) |     ts |   reward |   episode_reward_max |   episode_reward_min |   episode_len_mean |
-|-----------------------------+----------+---------------------+--------+------------------+--------+----------+----------------------+----------------------+--------------------|
-| PPO_CartPole-v0_21a72_00000 | RUNNING  | 10.148.8.236:218685 |      9 |          57.9681 | 127800 |      200 |                  200 |                  200 |                200 |
-+-----------------------------+----------+---------------------+--------+------------------+--------+----------+----------------------+----------------------+--------------------+
-
-
-Result for PPO_CartPole-v0_21a72_00000:
-  agent_timesteps_total: 142000
-  custom_metrics: {}
-  date: 2021-05-07_15-58-07
-  done: false
-  episode_len_mean: 200.0
-  episode_media: {}
-  episode_reward_max: 200.0
-  episode_reward_mean: 200.0
-  episode_reward_min: 200.0
-  episodes_this_iter: 71
-  episodes_total: 1554
-  experiment_id: 11a144229f2c421fbea9e1bcd7956598
-  hostname: r9i1n11
-  info:
-    learner:
-      default_policy:
-        learner_stats:
-          cur_kl_coeff: 0.01875000074505806
-          cur_lr: 4.999999873689376e-05
-          entropy: 0.49024271965026855
-          entropy_coeff: 0.0
-          kl: 0.003890060121193528
-          model: {}
-          policy_loss: -0.0013827681541442871
-          total_loss: 332.21807861328125
-          vf_explained_var: 0.4809533953666687
-          vf_loss: 332.21942138671875
-    num_agent_steps_sampled: 142000
-    num_steps_sampled: 142000
-    num_steps_trained: 142000
-  iterations_since_restore: 10
-  node_ip: 10.148.8.236
-  num_healthy_workers: 71
-  off_policy_estimator: {}
-  perf:
-    cpu_util_percent: 15.075
-    ram_util_percent: 9.0
-  pid: 218685
-  policy_reward_max: {}
-  policy_reward_mean: {}
-  policy_reward_min: {}
-  sampler_perf:
-    mean_action_processing_ms: 0.04392463467235637
-    mean_env_render_ms: 0.0
-    mean_env_wait_ms: 0.0568821864132888
-    mean_inference_ms: 0.6523705763050123
-    mean_raw_obs_processing_ms: 0.05978463113893993
-  time_since_restore: 63.5853476524353
-  time_this_iter_s: 5.617278575897217
-  time_total_s: 63.5853476524353
-  timers:
-    learn_throughput: 2576.2
-    learn_time_ms: 5511.995
-    sample_throughput: 18273.508
-    sample_time_ms: 777.081
-    update_time_ms: 6.758
-  timestamp: 1620424687
-  timesteps_since_restore: 0
-  timesteps_total: 142000
-  training_iteration: 10
-  trial_id: 21a72_00000
-  
-== Status ==
-Memory usage on this node: 16.9/187.3 GiB
-Using FIFO scheduling algorithm.
-Resources requested: 72.0/108 CPUs, 0/0 GPUs, 0.0/378.15 GiB heap, 0.0/166.06 GiB objects
-Result logdir: /scratch/eskordil/ray_results/CartPole-v0
-Number of trials: 1/1 (1 RUNNING)
-+-----------------------------+----------+---------------------+--------+------------------+--------+----------+----------------------+----------------------+--------------------+
-| Trial name                  | status   | loc                 |   iter |   total time (s) |     ts |   reward |   episode_reward_max |   episode_reward_min |   episode_len_mean |
-|-----------------------------+----------+---------------------+--------+------------------+--------+----------+----------------------+----------------------+--------------------|
-| PPO_CartPole-v0_21a72_00000 | RUNNING  | 10.148.8.236:218685 |     10 |          63.5853 | 142000 |      200 |                  200 |                  200 |                200 |
-+-----------------------------+----------+---------------------+--------+------------------+--------+----------+----------------------+----------------------+--------------------+
-
-
-Result for PPO_CartPole-v0_21a72_00000:
-  agent_timesteps_total: 156200
-  custom_metrics: {}
-  date: 2021-05-07_15-58-13
-  done: false
-  episode_len_mean: 200.0
-  episode_media: {}
-  episode_reward_max: 200.0
-  episode_reward_mean: 200.0
-  episode_reward_min: 200.0
-  episodes_this_iter: 71
-  episodes_total: 1625
-  experiment_id: 11a144229f2c421fbea9e1bcd7956598
-  hostname: r9i1n11
-  info:
-    learner:
-      default_policy:
-        learner_stats:
-          cur_kl_coeff: 0.00937500037252903
-          cur_lr: 4.999999873689376e-05
-          entropy: 0.5027791857719421
-          entropy_coeff: 0.0
-          kl: 0.0024740530643612146
-          model: {}
-          policy_loss: -0.0011052662739530206
-          total_loss: 389.9997253417969
-          vf_explained_var: 0.3971409797668457
-          vf_loss: 390.00091552734375
-    num_agent_steps_sampled: 156200
-    num_steps_sampled: 156200
-    num_steps_trained: 156200
-  iterations_since_restore: 11
-  node_ip: 10.148.8.236
-  num_healthy_workers: 71
-  off_policy_estimator: {}
-  perf:
-    cpu_util_percent: 14.400000000000002
-    ram_util_percent: 9.0
-  pid: 218685
-  policy_reward_max: {}
-  policy_reward_mean: {}
-  policy_reward_min: {}
-  sampler_perf:
-    mean_action_processing_ms: 0.04395001201863764
-    mean_env_render_ms: 0.0
-    mean_env_wait_ms: 0.05689468077693932
-    mean_inference_ms: 0.6509330989461597
-    mean_raw_obs_processing_ms: 0.05956283527205119
-  time_since_restore: 69.32215785980225
-  time_this_iter_s: 5.736810207366943
-  time_total_s: 69.32215785980225
-  timers:
-    learn_throughput: 2595.025
-    learn_time_ms: 5472.01
-    sample_throughput: 78934.079
-    sample_time_ms: 179.897
-    update_time_ms: 6.795
-  timestamp: 1620424693
-  timesteps_since_restore: 0
-  timesteps_total: 156200
-  training_iteration: 11
-  trial_id: 21a72_00000
-  
-== Status ==
-Memory usage on this node: 16.9/187.3 GiB
-Using FIFO scheduling algorithm.
-Resources requested: 72.0/108 CPUs, 0/0 GPUs, 0.0/378.15 GiB heap, 0.0/166.06 GiB objects
-Result logdir: /scratch/eskordil/ray_results/CartPole-v0
-Number of trials: 1/1 (1 RUNNING)
-+-----------------------------+----------+---------------------+--------+------------------+--------+----------+----------------------+----------------------+--------------------+
-| Trial name                  | status   | loc                 |   iter |   total time (s) |     ts |   reward |   episode_reward_max |   episode_reward_min |   episode_len_mean |
-|-----------------------------+----------+---------------------+--------+------------------+--------+----------+----------------------+----------------------+--------------------|
-| PPO_CartPole-v0_21a72_00000 | RUNNING  | 10.148.8.236:218685 |     11 |          69.3222 | 156200 |      200 |                  200 |                  200 |                200 |
-+-----------------------------+----------+---------------------+--------+------------------+--------+----------+----------------------+----------------------+--------------------+
-
-
-Result for PPO_CartPole-v0_21a72_00000:
-  agent_timesteps_total: 170400
-  custom_metrics: {}
-  date: 2021-05-07_15-58-19
-  done: false
-  episode_len_mean: 200.0
-  episode_media: {}
-  episode_reward_max: 200.0
-  episode_reward_mean: 200.0
-  episode_reward_min: 200.0
-  episodes_this_iter: 71
-  episodes_total: 1696
-  experiment_id: 11a144229f2c421fbea9e1bcd7956598
-  hostname: r9i1n11
-  info:
-    learner:
-      default_policy:
-        learner_stats:
-          cur_kl_coeff: 0.004687500186264515
-          cur_lr: 4.999999873689376e-05
-          entropy: 0.4912486672401428
-          entropy_coeff: 0.0
-          kl: 0.002670838963240385
-          model: {}
-          policy_loss: -0.0010692896321415901
-          total_loss: 388.764892578125
-          vf_explained_var: 0.36530521512031555
-          vf_loss: 388.7658386230469
-    num_agent_steps_sampled: 170400
-    num_steps_sampled: 170400
-    num_steps_trained: 170400
-  iterations_since_restore: 12
-  node_ip: 10.148.8.236
-  num_healthy_workers: 71
-  off_policy_estimator: {}
-  perf:
-    cpu_util_percent: 14.662499999999998
-    ram_util_percent: 9.0
-  pid: 218685
-  policy_reward_max: {}
-  policy_reward_mean: {}
-  policy_reward_min: {}
-  sampler_perf:
-    mean_action_processing_ms: 0.04395983159364339
-    mean_env_render_ms: 0.0
-    mean_env_wait_ms: 0.05692901775875984
-    mean_inference_ms: 0.6502541526021983
-    mean_raw_obs_processing_ms: 0.059432863135267286
-  time_since_restore: 75.09779334068298
-  time_this_iter_s: 5.775635480880737
-  time_total_s: 75.09779334068298
-  timers:
-    learn_throughput: 2591.391
-    learn_time_ms: 5479.682
-    sample_throughput: 79030.998
-    sample_time_ms: 179.676
-    update_time_ms: 6.942
-  timestamp: 1620424699
-  timesteps_since_restore: 0
-  timesteps_total: 170400
-  training_iteration: 12
-  trial_id: 21a72_00000
-  
-== Status ==
-Memory usage on this node: 16.9/187.3 GiB
-Using FIFO scheduling algorithm.
-Resources requested: 72.0/108 CPUs, 0/0 GPUs, 0.0/378.15 GiB heap, 0.0/166.06 GiB objects
-Result logdir: /scratch/eskordil/ray_results/CartPole-v0
-Number of trials: 1/1 (1 RUNNING)
-+-----------------------------+----------+---------------------+--------+------------------+--------+----------+----------------------+----------------------+--------------------+
-| Trial name                  | status   | loc                 |   iter |   total time (s) |     ts |   reward |   episode_reward_max |   episode_reward_min |   episode_len_mean |
-|-----------------------------+----------+---------------------+--------+------------------+--------+----------+----------------------+----------------------+--------------------|
-| PPO_CartPole-v0_21a72_00000 | RUNNING  | 10.148.8.236:218685 |     12 |          75.0978 | 170400 |      200 |                  200 |                  200 |                200 |
-+-----------------------------+----------+---------------------+--------+------------------+--------+----------+----------------------+----------------------+--------------------+
-
-

From 6657e2da415b8cd694e438029c59fb5051a9e1ab Mon Sep 17 00:00:00 2001
From: zxymark221 <zxymark@vt.edu>
Date: Wed, 6 Mar 2024 14:25:14 -0700
Subject: [PATCH 12/17] rllib update: add env requirements.txt

---
 .gitignore                              |  2 ++
 languages/python/RLlib/README.md        |  8 +++++++-
 languages/python/RLlib/env_example.yml  | 16 ----------------
 languages/python/RLlib/requirements.txt |  9 +++++++++
 4 files changed, 18 insertions(+), 17 deletions(-)
 delete mode 100644 languages/python/RLlib/env_example.yml
 create mode 100644 languages/python/RLlib/requirements.txt

diff --git a/.gitignore b/.gitignore
index 97c429a1d..88cba29bb 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,2 +1,4 @@
 .*
 !/.gitignore
+**.pyc
+**/__pycache__
diff --git a/languages/python/RLlib/README.md b/languages/python/RLlib/README.md
index 1032a0195..01a564dcd 100644
--- a/languages/python/RLlib/README.md
+++ b/languages/python/RLlib/README.md
@@ -32,7 +32,13 @@ conda create --prefix=/projects/$HPC_HANDLE/$USER/conda_envs/rl_hpc python=3.10
 
 Here, `$HPC_HANDLE` is the project handle and `$USER` is your HPC user name.
 
-#TODO: adding the script installing necessary packages.
+Activate the conda environment and install packages:
+
+```
+conda activate /projects/$HPC_HANDLE/$USER/conda_envs/rl_hpc
+
+pip install -r requirements.txt
+```
 
 ### 3rd step: Test OpenAI Gym API
 
diff --git a/languages/python/RLlib/env_example.yml b/languages/python/RLlib/env_example.yml
deleted file mode 100644
index 55831058e..000000000
--- a/languages/python/RLlib/env_example.yml
+++ /dev/null
@@ -1,16 +0,0 @@
-name: env_example
-channels:
- - conda-forge
- - defaults
-dependencies:
- - python=3.7
- - gym
- - jupyterlab
- - pandas
- - scikit-learn
- - jupyter
- - notebook
- - pip
- - pip:
-   - ray[rllib,debug,tune]
-   - tensorflow==2.4.0
diff --git a/languages/python/RLlib/requirements.txt b/languages/python/RLlib/requirements.txt
new file mode 100644
index 000000000..4091aa271
--- /dev/null
+++ b/languages/python/RLlib/requirements.txt
@@ -0,0 +1,9 @@
+gymnasium==0.28.1
+ray==2.9.2
+torch==2.2.0
+pygame==2.5.2
+dm-tree==0.1.8
+pyarrow==15.0.0
+pandas==2.2.0
+scikit-image==0.22.0
+tensorboard==2.16.2

From dd623a90f1252d727c17eeb1e9e5b658c6749449 Mon Sep 17 00:00:00 2001
From: zxymark221 <zxymark@vt.edu>
Date: Wed, 6 Mar 2024 15:38:20 -0700
Subject: [PATCH 13/17] rllib update: Update readmes.

---
 languages/python/RLlib/README.md              | 11 ++++++
 .../python/RLlib/custom_gym_env/README.md     |  8 +++++
 languages/python/RLlib/test/README.md         | 34 +++++++++++++++++++
 languages/python/RLlib/train/README.md        |  9 +++++
 4 files changed, 62 insertions(+)
 create mode 100644 languages/python/RLlib/test/README.md

diff --git a/languages/python/RLlib/README.md b/languages/python/RLlib/README.md
index 01a564dcd..996ebd649 100644
--- a/languages/python/RLlib/README.md
+++ b/languages/python/RLlib/README.md
@@ -92,3 +92,14 @@ The following libraries should also be imported without an error.
 import ray
 import torch
 ```
+
+## Use this tutorial
+
+We suggest follow this tutorial with the following order:
+
+1. Understand how to build a custom environment for your problem. Detailed guidelines are provided [here](custom_gym_env/README.md).
+
+2. Train the RL agent/policy/controller by following [this guideline](train/README.md).
+
+3. Test the trained RL agent as explained [here](test/README.md).
+
diff --git a/languages/python/RLlib/custom_gym_env/README.md b/languages/python/RLlib/custom_gym_env/README.md
index 6fb545f5d..cf519ec13 100644
--- a/languages/python/RLlib/custom_gym_env/README.md
+++ b/languages/python/RLlib/custom_gym_env/README.md
@@ -90,3 +90,11 @@ This tutorial provides an example of customized environment called "CarPass", le
 See the [full implementation](custom_env.py) for details, and the comments and docstrings should provide some explanations. 
 
 One thing to notice is that there is a `render()` function in the customized environment, which is used to render the 2D image as shown above for visualization purpose. It is optional, so no need to implement `render()` if not necessary.
+
+## More examples
+
+Grid:
+- Distribution System critical load restoration environment, see [this repo](https://github.com/NREL/rlc4clr/tree/main/rlc4clr/clr_envs/envs).
+
+Building Control:
+- Five-zone building HVAC Control, see [this file](https://github.com/NREL/learning-building-control/blob/main/lbc/building_env.py). This example still uses OpenAI Gym API instead of Gymnasium API though.
diff --git a/languages/python/RLlib/test/README.md b/languages/python/RLlib/test/README.md
new file mode 100644
index 000000000..b09498102
--- /dev/null
+++ b/languages/python/RLlib/test/README.md
@@ -0,0 +1,34 @@
+## Rollout trained policy
+
+This section explains once an RL agent (or policy) is trained, how do we use it to rollout the environment, i.e., examine how the control is implemented.
+
+### Set up configuration
+
+```
+# Use the same configuration as the one for training except the rollout worker
+# number, which can be limited at 1.
+config = (
+    get_trainable_cls("PPO")
+    .get_default_config()
+    .framework("torch")
+    .training(model={"fcnet_hiddens": [256, 256]})  # Same as the one in training
+    .rollouts(num_rollout_workers=1)
+)
+```
+
+Note, the number of rollout workers does not have to be the same as the value used for training, and it can be as low as 1. If setting to a high value, Ray will start that many rollout workers, which is unnecessary when rollout.
+
+### Load trained RL agent
+
+The trained RL agent can be restored as shown below:
+
+```
+agent = PPO(config, env=env_name)
+checkpoint_path = ...
+agent.restore(checkpoint_path)
+```
+See [the code](rollout.py) for detailed example of setting checkpoint path.
+
+### Obtain action from the trained policy
+
+The action can be retrieved by calling `agent.compute_single_action(state)`, here we used state and observation interchangably. By passing in the environment state/observation, the agent returns the action according to the trained policy.
diff --git a/languages/python/RLlib/train/README.md b/languages/python/RLlib/train/README.md
index 7cc8a4946..50d6d9cef 100644
--- a/languages/python/RLlib/train/README.md
+++ b/languages/python/RLlib/train/README.md
@@ -162,3 +162,12 @@ Remember to replace `dummynodeid` above with your actual computing node's ID. Th
     <em>You can visit Tensorboard from `localhost:16006` while the server runs on a computing node.</em>
 </p>
 
+### 5. Local computer training
+
+To train on local computer, the following command can be used:
+
+```
+python train_script.py --worker-num 5
+```
+
+The worker number needs to be specified here because otherwise the default value (102) will be used, see the [default configuration](config_parser.py). Since local computer usually don't have this many CPU cores, the training will hang in there (waiting for resources that will never come). So, using a smaller number (less than the CPU cores number) is needed for local training.

From 3db402db8489555775179c901921dbac9531fa1f Mon Sep 17 00:00:00 2001
From: zxymark221 <zxymark@vt.edu>
Date: Tue, 19 Mar 2024 08:42:33 -0600
Subject: [PATCH 14/17] rllib update: revise the main readme.

---
 languages/python/RLlib/README.md | 26 +++++++++++++++-----------
 1 file changed, 15 insertions(+), 11 deletions(-)

diff --git a/languages/python/RLlib/README.md b/languages/python/RLlib/README.md
index 996ebd649..3626be11d 100644
--- a/languages/python/RLlib/README.md
+++ b/languages/python/RLlib/README.md
@@ -1,3 +1,17 @@
+## Use this tutorial
+
+This tutorial provides examples on how to use [RLlib](https://docs.ray.io/en/master/rllib/) for reinforcement learning, with an emphasis on building customized environments for your own optimal control problems. The tutorial is developed assuming using NREL HPC system Kestrel, but it can be easily modified to be able to run on a local computer.
+
+We suggest follow this tutorial with the following order:
+
+1. Understand how to build a custom environment for your problem. Detailed guidelines are provided [here](custom_gym_env/README.md).
+
+2. Train the RL agent/policy/controller by following [this guideline](train/README.md).
+
+3. Test the trained RL agent as explained [here](test/README.md).
+
+But before that, please follow the instructions below to set up a Python Conda environment.
+
 ## Create Anaconda environment
 
 Follow the following steps to create an Anaconda environment for this experiment:
@@ -21,7 +35,7 @@ module purge
 module load anaconda3
 ```
 
-We suggest creating a conda environment on your `\projects` rather than `\home` or `\scratch`. (#TODO: Check this with HPC team.)
+We suggest creating a conda environment on your `\projects` rather than `\home` or `\scratch`.
 
 ***Example:***
 
@@ -93,13 +107,3 @@ import ray
 import torch
 ```
 
-## Use this tutorial
-
-We suggest follow this tutorial with the following order:
-
-1. Understand how to build a custom environment for your problem. Detailed guidelines are provided [here](custom_gym_env/README.md).
-
-2. Train the RL agent/policy/controller by following [this guideline](train/README.md).
-
-3. Test the trained RL agent as explained [here](test/README.md).
-

From 01a8c1c9c9277fc40f25dda8b06684fe3b0f4992 Mon Sep 17 00:00:00 2001
From: zxymark221 <zxymark@vt.edu>
Date: Tue, 19 Mar 2024 09:55:11 -0600
Subject: [PATCH 15/17] rllib update: minor changes on gym env animation
 rendering setting.

---
 languages/python/RLlib/custom_gym_env/custom_env.py | 11 +++++++++--
 languages/python/RLlib/test/rollout.py              | 13 ++++++++++---
 2 files changed, 19 insertions(+), 5 deletions(-)

diff --git a/languages/python/RLlib/custom_gym_env/custom_env.py b/languages/python/RLlib/custom_gym_env/custom_env.py
index 923f0257b..490bf22c5 100644
--- a/languages/python/RLlib/custom_gym_env/custom_env.py
+++ b/languages/python/RLlib/custom_gym_env/custom_env.py
@@ -323,15 +323,22 @@ def close(self):
     done = False
     episodic_reward = 0.0
 
+    # Set the rendering flag to True if you are on local computer and would
+    # love to see the rendered animation. Default is False, assuming you are
+    # on Kestrel.
+    RENDER = False
+
     while not done:
-        env.render()
+        if RENDER:
+            env.render()
         act = env.action_space.sample()
         obs, reward, terminated, truncated, info = env.step(act)
         print(obs)
         done = (terminated or truncated)
         episodic_reward += reward
     
-    env.render()
+    if RENDER:
+        env.render()
 
     print("Reward this episode is %f" % episodic_reward)
     print("Steps this episode is %d" % env.step_count)
diff --git a/languages/python/RLlib/test/rollout.py b/languages/python/RLlib/test/rollout.py
index c2279f8bf..5d670d98e 100644
--- a/languages/python/RLlib/test/rollout.py
+++ b/languages/python/RLlib/test/rollout.py
@@ -10,14 +10,15 @@
 CURRENT_FILE_DIR = os.path.dirname(os.path.abspath(__file__))
 
 env_name = 'CarPass-v0'
-def env_creator(config):
 
+def env_creator(config):
     custom_env_dir = CURRENT_FILE_DIR.replace('test', 'custom_gym_env')
     sys.path.append(os.path.dirname(custom_env_dir))
 
     from custom_gym_env.custom_env import CarPassEnv
     env = CarPassEnv()
     return env
+
 register_env(env_name, env_creator)
 
 # Use the same configuration as the one for training except the rollout worker
@@ -45,12 +46,18 @@ def env_creator(config):
 truncated = False
 total_rew = 0.0
 
+# Set the rendering flag to True if you are on local computer and would
+# love to see the rendered animation. Default is False, assuming you are
+# on Kestrel.
+RENDER = False
+
 while not (terminated or truncated):
     
     act = agent.compute_single_action(state)
     # print(act)
-    state, reward, terminated, truncated, info = env.step(act)
-    env.render()
+    state, reward, terminated, truncated, info = env.step(act)   
     total_rew += reward
+    if RENDER:
+        env.render()
 
 print(total_rew)

From 6a3ef6b92ca41efc141f4f0f0cf7b108817699e7 Mon Sep 17 00:00:00 2001
From: zxymark221 <zxymark@vt.edu>
Date: Tue, 19 Mar 2024 11:34:47 -0600
Subject: [PATCH 16/17] rllib update: update setup info in README.

---
 languages/python/RLlib/README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/languages/python/RLlib/README.md b/languages/python/RLlib/README.md
index 3626be11d..103d95283 100644
--- a/languages/python/RLlib/README.md
+++ b/languages/python/RLlib/README.md
@@ -41,6 +41,7 @@ We suggest creating a conda environment on your `\projects` rather than `\home`
 
 Use the following script to create a conda environment:
 ```
+mkdir -p /projects/$HPC_HANDLE/$USER/conda_envs
 conda create --prefix=/projects/$HPC_HANDLE/$USER/conda_envs/rl_hpc python=3.10
 ```
 

From e4ff69cea6f58891d9ceef78cc151b46cec08401 Mon Sep 17 00:00:00 2001
From: zxymark221 <zxymark@vt.edu>
Date: Tue, 19 Mar 2024 11:40:40 -0600
Subject: [PATCH 17/17] rllib update: update the sbatch script to request /tmp
 for RLlib.

---
 languages/python/RLlib/train/train_multi_nodes.sh | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/languages/python/RLlib/train/train_multi_nodes.sh b/languages/python/RLlib/train/train_multi_nodes.sh
index a9f789a08..7937c425c 100644
--- a/languages/python/RLlib/train/train_multi_nodes.sh
+++ b/languages/python/RLlib/train/train_multi_nodes.sh
@@ -2,8 +2,9 @@
 #SBATCH --account=$HPC_HANDLE
 #SBATCH --time=1:00:00
 #SBATCH --job-name=rl_train
-#SBATCH --nodes=10
-#SBATCH --partition=short
+#SBATCH --nodes=2
+#SBATCH --partition=debug
+#SBATCH --tmp=160000
 #SBATCH --tasks-per-node=1
 
 # Step 1. Loading conda env
@@ -58,4 +59,4 @@ echo "Start training"
 
 TIME=$(squeue -j $SLURM_JOB_ID -h --Format TimeLimit)
 
-python -u train_script.py --run PPO --redis-password $redis_password --worker-num $worker_num --ip-head $ip_head
+python -u train_script.py --redis-password $redis_password --worker-num $worker_num --ip-head $ip_head