numpy.random.generator should be preferred to numpy.random.random state

scripts/train.py

@@ -118,6 +118,11 @@ def clear_tm_instances():
     _, uncompiled_shared_network = make_untrained_iqn_network(jit=config_copy.use_jit, is_inference=False)
     uncompiled_shared_network.share_memory()
 
+    # init random number generator
+    seed = 275328254363729247691611008422666101254
+    # creating the RNG that is passed around. spawn() will create new independent child generators from it
+    rng = np.random.default_rng(seed)
+
     # Start learner process
     learner_process = mp.Process(
         target=learner_process_fn,
@@ -129,6 +134,7 @@ def clear_tm_instances():
             base_dir,
             save_dir,
             tensorboard_base_dir,
+            rng.spawn(1)[0],
         ),
     )
     learner_process.start()
@@ -148,6 +154,7 @@ def clear_tm_instances():
                 base_dir,
                 save_dir,
                 config_copy.base_tmi_port + process_number,
+                rng.spawn(1)[0],
             ),
         )
         for rollout_queue, process_number in zip(rollout_queues, range(config_copy.gpu_collectors_count))

trackmania_rl/agents/iqn.py

@@ -4,6 +4,7 @@
     - The Trainer class, which implements the IQN training logic in method train_on_batch.
     - The Inferer class, which implements utilities for forward propagation with and without exploration.
 """
+
 import copy
 import math
 import random
@@ -350,15 +351,17 @@ class Inferer:
         "epsilon_boltzmann",
         "tau_epsilon_boltzmann",
         "is_explo",
+        "_rng",
     )
 
-    def __init__(self, inference_network, iqn_k, tau_epsilon_boltzmann):
+    def __init__(self, inference_network, iqn_k, tau_epsilon_boltzmann, rng: np.random.Generator):
         self.inference_network = inference_network
         self.iqn_k = iqn_k
         self.epsilon = None
         self.epsilon_boltzmann = None
         self.tau_epsilon_boltzmann = tau_epsilon_boltzmann
         self.is_explo = None
+        self._rng = rng
 
     def infer_network(self, img_inputs_uint8: npt.NDArray, float_inputs: npt.NDArray, tau=None) -> npt.NDArray:
         """
@@ -415,9 +418,9 @@ def get_exploration_action(self, img_inputs_uint8: npt.NDArray, float_inputs: np
 
         if self.is_explo and r < self.epsilon:
             # Choose a random action
-            get_argmax_on = np.random.randn(*q_values.shape)
+            get_argmax_on = self._rng.standard_normal(*q_values.shape)
         elif self.is_explo and r < self.epsilon + self.epsilon_boltzmann:
-            get_argmax_on = q_values + self.tau_epsilon_boltzmann * np.random.randn(*q_values.shape)
+            get_argmax_on = q_values + self.tau_epsilon_boltzmann * self._rng.standard_normal(*q_values.shape)
         else:
             get_argmax_on = q_values