A fork of the Ray framework that aims to implements two-players AlphaZero algorithm

In ray, by default, the alpha-zero algorithm is for one-player game. Now, you can specify if your game is made for one or two players. You just have to set the mcts parameter is_two_players to True

Example:

    # === MCTS ===
    "mcts_config": {
        "puct_coefficient": 1.0,
        "num_simulations": 30,
        "temperature": 1.5,
        "dirichlet_epsilon": 0.25,
        "dirichlet_noise": 0.03,
        "argmax_tree_policy": False,
        "add_dirichlet_noise": True,
        "is_two_players": True,
    }

Example games

Some games along with their trained agent are implemented in the examples directory.

• Connect2 (To win, connect two token of your color on a board mode of 1 row and 4 columns)
• Tic Tac Toe
• Connect4

For Connect2 and TicTacToe, their trained agent plays perfectly.

Ray provides a simple, universal API for building distributed applications.

Ray is packaged with the following libraries for accelerating machine learning workloads:

• Tune: Scalable Hyperparameter Tuning
• RLlib: Scalable Reinforcement Learning
• Train: Distributed Deep Learning (beta)
• Datasets: Distributed Data Loading and Compute

As well as libraries for taking ML and distributed apps to production:

• Serve: Scalable and Programmable Serving
• Workflows: Fast, Durable Application Flows (alpha)

There are also many community integrations with Ray, including Dask, MARS, Modin, Horovod, Hugging Face, Scikit-learn, and others. Check out the full list of Ray distributed libraries here.