AlphaZero implementation based on "Mastering the game of Go without human knowledge" and "Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm" by DeepMind.

The algorithm learns to play games like Chess and Go without any human knowledge. It uses Monte Carlo Tree Search and a Deep Residual Network to evaluate the board state and play the most promising move.

Games implemented:

1. Tic Tac Toe
2. Othello
3. Connect Four

• TensorFlow (Tested on 1.4.0)
• NumPy
• Python 3

To train the model from scratch.:

python main.py --load_model 0

To train the model using the previous best model as a starting point:

python main.py --load_model 1

To play a game vs the previous best model:

python main.py --load_model 1 --human_play 1

Options:

• --num_iterations: Number of iterations.
• --num_games: Number of self play games played during each iteration.
• --num_mcts_sims: Number of MCTS simulations per game.
• --c_puct: The level of exploration used in MCTS.
• --l2_val: The level of L2 weight regularization used during training.
• --momentum: Momentum Parameter for the momentum optimizer.
• --learning_rate: Learning Rate for the momentum optimizer.
• --t_policy_val: Value for policy prediction.
• --temp_init: Initial Temperature parameter to control exploration.
• --temp_final: Final Temperature parameter to control exploration.
• --temp_thresh: Threshold where temperature init changes to final.
• --epochs: Number of epochs during training.
• --batch_size: Batch size for training.
• --dirichlet_alpha: Alpha value for Dirichlet noise.
• --epsilon: Value of epsilon for calculating Dirichlet noise.
• --model_directory: Name of the directory to store models.
• --num_eval_games: Number of self-play games to play for evaluation.
• --eval_win_rate: Win rate needed to be the best model.
• --load_model: Binary to initialize the network with the best model.
• --human_play: Binary to play as a Human vs the AI.
• --resnet_blocks: Number of residual blocks in the resnet.
• --record_loss: Binary to record policy and value loss to a file.
• --loss_file: Name of the file to record loss.
• --game: Number of the game. 0: Tic Tac Toe, 1: Othello.

MIT License

Copyright (c) 2018 Blanyal D'Souza

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
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copies of the Software, and to permit persons to whom the Software is
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The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

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