General Tree Evaluation for AlphaZero

Introduction

Welcome to the official repository for General Tree Evaluation for AlphaZero, a novel approach to enhancing model-based deep reinforcement learning. This repository contains the code and resources developed as part of the research that extends and refines the AlphaZero algorithm, particularly focusing on decoupling tree construction from action policies.

These plots demonstrate the empirical benefits of our proposed policies in classical Gym environments, especially under constrained simulation budgets.

Capabilities

Logging to TensorBoard
Logging to Weights & Biases
Customizable environment configurations
... and more!

Getting Started

For a quick start, feel free to use the notebook kaggle.ipynb which should run on Kaggle/Google Colab with minimal setup. If you want to run the code locally, follow the instructions below.

Local Installation

Start by cloning the repository:

git clone https://github.com/albinjal/GeneralAlphaZero.git
cd GeneralAlphaZero

You can set up the environment using either Pip or Conda. Choose one of the following methods to install the dependencies:

Using Pip

Create a virtual environment and activate it:

python3 -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

Install the dependencies:

pip install -r requirements.txt

Using Conda

Create and activate the Conda environment:

conda env create -f environment.yml
conda activate az10

File Structure

TODO

Contribution

Contributions are welcome! Please open an issue or submit a pull request for any improvements, bug fixes, or new features.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Feel free to explore and modify the code, and don't hesitate to reach out if you have any questions or need further assistance. Happy coding!

albinjal / GeneralAlphaZero