Reinforcement-Learning-course

This repository contains an introductory course to Reinforcement Learning (RL) with hands-on classic examples of agents trained on gymnasium environments.

Dynamic Programming

Use file frozen_lake.py to train a Dynamic Programming agent on the FrozenLake environment, argument algorithm specifies which algorithm to use between value_iteration, q_iteration and policy_oteration. Example:

cd dynamic-programming
python frozen_lake.py --map_name 4x4 --algorithm policy_iteration

DQN

Use file lunar_lander.py to train a DQN agent on the LunarLander environment. Example:

cd deep_rl/dqn
python lunar_lander.py --n_train 100000