alpha-zero
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:
- Tic Tac Toe
- Othello
- Connect Four
Requirements
- TensorFlow (Tested on 1.4.0)
- NumPy
- Python 3
Usage
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.
License
MIT License
Copyright (c) 2018 Blanyal D'Souza
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