Tabular methods for reinforcement learning

This repo implements Deep Q-Network (DQN) for solving the Cliff Walking v0 environment of the Gymnasium library using Python 3.8 and PyTorch 2.0.1 with the finest tuning.

Simple implementation and comparison of three reinforcement learning models.

基于pytoch的俩个简单的强化学习案例

This project utilizes Markov Decision Process (MDP) principles to implement a custom "CliffWalking" environment in Gym, employing policy iteration to find an optimal policy for agent navigation.

AI related graph search algorithms with step-by-step implementation as well as comparison between different methods for cliff-walker problem.

Solutions for Reinforcement learning lab-exam 2019

Cliffwalk to compare SARSA and Q-Learning

Use cliff walking to compare the difference between Q-learning and SARSA algorithms in Reinforcement Learning

Reinforcement Learning basic tasks

simple cliff walk implementation

Temporal Difference methods - A simple implementation of SARSA algorithm applied to OpenAI gym's "CliffWalking" environment.

AI application of Min-Max algorithm including alpha-beta pruning approach for two agents in cliff-walker scenario