guidogiacomomussini/reinfocementlearning--chess_engine

Reinforcement Learning-based chess engine to solve chess finals.

Reinfocement-Learning__Chess-Engine

A reinforcement learning problem involving chess.

By me @GuidoGiacomoMussini and @mbarte.

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In this project we implemented from scratch a small chess engine (white agent), which is able to play and win the final in the picture below against Stockfish 15.1.

Starting position.

In order for a user to run the code, he should download and install Stockfish.

Game results trend.

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• dynamics_br: implementation of the "backbone" of the game from scratch (dictionaries to define pieces and their moves, functions to retrieve possible moves and allowed future states, move pieces, etc.).

• algorithm_utils_br: functions used by the learning algorithm scarsa_lambda_br.

• scarsa_lambda_br: learning algorithm (sarsa-lambda, with epsilon-greedy approach).

• executable_br: Jupyter Notebook to train the agent.

• user_vs_scarsa_lambda_br: adaptation of scarsa_lambda_br replacing Stockfish with input from keyboard to allow users to play against our trained agent (one should download the Q_s_20t_1000g.pickle file, containing the "parameters" of the trained agent).

• game_br: Jupyter Notebook to play against our trained agent.

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• Q_s_20t_1000g: Dictionary with the trained agent's parameters.

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Future work: longer training against Stockfish with variable ELO, so that our model learns to play against a potential human user. In fact, in a sense it is easier to learn to beat Stockfish because of it's predictability, due to the constant performance of the best moves.