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 dqn
python lunar_lander.py --n_train 1000

For DQN, there is an argument log_dir that specifies the name of a folder where tensorboard events will be stored. To use tensorboard, let us suppose that we specified --log_dir runs_agent, then we can track the evolution of some variables during training by entering:

tensorboard --logdir runs_agent

A message will then be displayed to describe how to open the localhost to visualise the tracked variables.