Alpha Zero General is a powerful framework designed for implementing self-play based reinforcement learning that can be adopted for any two-player turn-based adversarial game. Inspired by the AlphaGo Zero paper, this implementation introduces a flexible and easy-to-understand setup for building intelligent game-playing agents. In this article, we will guide you on how to utilize this framework, including a sample implementation in PyTorch and Keras.

Getting Started: Setting Up the Framework

To kick off your adventure with Alpha Zero, follow these straightforward steps:

• Clone the Repository: You can find the code repository here.
• Choose Your Framework: Decide whether to use PyTorch or Keras, as both options are available.
• Subclassing: To implement your game, subclass the classes in Game.py and NeuralNet.py and implement their functions.
• Sample Implementations: For a practical example, refer to othelloOthelloGame.py for game logic and othellopytorch,kerasNNet.py for the neural network setup.

Training Your Model

To start model training for the game of Othello, follow these commands:

bash
python main.py

In main.py, you can choose your game and framework. Adjust the parameters as necessary to customize your training process.

Docker Installation

Setting up your environment can be easily achieved using nvidia-docker. Once installed:

setup_env.sh

This command will prepare a Jupyter Docker container with your selected framework. Then you can run the training by executing:

docker exec -ti pytorch_notebook python main.py

Understanding the Code: An Analogy

Let’s compare the implementation of Alpha Zero with a master chef preparing a unique dish. Just as a chef needs a recipe, ingredients, and cooking techniques, Alpha Zero requires a well-defined game, neural network architecture, and effective training loops. Here’s how it breaks down:

• Recipe (Game.py and NeuralNet.py): These are the core instructions outlining how the game is played and how the AI evaluates the game state.
• Ingredients (Parameters): Much like having the right ingredients for a dish, tuning parameters such as learning rate, batch size, and MCTS simulations impact how well the AI learns.
• Cooking Techniques (MCTS.py and Coach.py): The cooking techniques, like Monte Carlo Tree Search and the training loop, are what ensure the AI develops its strategy over time, refining its approach to become a skilled player.

Conducting Experiments

To see the effectiveness of your model, you can train it in various configurations. A model trained for 6×6 Othello took approximately 3 days using 80 iterations, with 100 episodes per iteration and 25 MCTS simulations per turn using an NVIDIA Tesla K80.

Feel free to utilize the pretrained model available in pretrained_models/othellopytorch and challenge it using pit.py for some engaging gameplay!

Troubleshooting

If you encounter any issues during the setup or execution process, consider the following troubleshooting tips:

• Dependencies: Ensure all required libraries and dependencies are installed correctly.
• Permissions: If you have trouble executing scripts, check your file permissions.
• Framework Compatibility: Verify that the chosen framework version is compatible with your Python setup.
• Configurations: Adjust parameters within main.py to see if different settings yield better results.

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