The Custom Gridworld and Environment Demo of Ship Route Planning with Reinforcement Learning. The reinforcement learning based on Qlearning method is realized. Q tables can be saved. Support documentation of training sessions. Support the display of result graphs

solving a simple 4*4 Gridworld almost similar to openAI gym FrozenLake using Qlearning Temporal difference method Reinforcement Learning

Application of Q-Learning method on Gridworld environment

QLearning implementation reinforced by norms in a multi-agent simulation

This project is from one of my master's degree projects. It is about pathfinding with using Q learning. Implemented it in unity. C#

This is using the UC Berkeley codebase for the PacMan AI project. This project utilizes search algorithms for artificial intelligence agents, and utilizes reinforcement learning.

Creating a reinforcement learning agent that plays 5x5 Go game using Q-Learning.

Implementing a reinforcement learning agent in a grid world that earns rewards and faces penalties

A very simple playground to experiment RL algorithms

𝙵𝚎𝚍𝚎𝚛𝚊𝚝𝚎𝚍 𝙼𝙰𝚁𝙻-𝙶𝚢𝚖 : We introduce a custom multi-agent reinforcement learning environment built with Gymnasium and Pygame, designed for evaluating federated RL (FRL) algorithms. The environment models a grid world where multiple agents—such as robots—navigate to accomplish spatially distributed tasks, like reaching delivery points.

Defining and Solving RL Environments

A Python-based repository with implementations of RL algorithms, featuring visualization tools and benchmarks

An AI agent learns to solve mazes using SARSA, Q-Learning, and Deep Q-Learning through exploration, rewards, and neural intelligence.