RL-Basics

Assignment for Reinforcement Learning Masters' course.

Three different implementations of classic RL-Algorithms on games such as Tic-Tac-Toe and Connect4.

Read the Report.pdf for more information about the methods.

1. You can run Demo.py for a small presentation of our demo-agents.
2. You can run pit.py by setting the players you want to run. Load the agents from the folder Agents!
3. The folder Agents contains all the different agents we used for experiments.
4. The folder DEMO contains the agents without time constraints, these agents are not used in any of the experiments and they are just for demo purpose. Please do not use these agents in the Pit.
5. The folder experiments contains all the different experiments we did for each one of the agents. The results are gathered in the jupyter notebook for visualizing the plots.

Essential software: Python 3.6.0, numpy 1.13.3, pandas 0.23.4, joblib 0.11, tqdm 4.31.1, seaborn 0.9.0 (for the plots).