Efficient Monte Carlo Tree Search via On-the-Fly State-Conditioned Action Abstraction

This repository contains the official implementation of the publication: Efficient Monte Carlo Tree Search via On-the-Fly State-Conditioned Action Abstraction, Yunhyeok Kwak, Inwoo Hwang, Dooyoung Kim, Sanghack Lee, Byoung-Tak Zhang, The 40th Conference on Uncertainty in Artificial Intelligence, 2024.

📦 Installation

• Use the Docker image (recommended): yunkwak/efficient-mcts:1.0 (Docker Hub)
• Or, install the dependencies manually. JAX should be installed separately. (Tested on Python 3.10, jax==0.4.16, haiku==0.0.10): .devcontainer/requirements.txt

🚀 Quick Start

To run the experiments, use the following commands:

python scripts/run_experiment.py

✒️ Citation

If you use this code in your research, please cite the following paper:

@inproceedings{
    kwak2024efficient,
    title={Efficient Monte Carlo Tree Search via On-the-Fly State-Conditioned Action Abstraction},
    author={Yunhyeok Kwak and Inwoo Hwang and Dooyoung Kim and Sanghack Lee and Byoung-Tak Zhang},
    booktitle={The 40th Conference on Uncertainty in Artificial Intelligence},
    year={2024},
    url={https://openreview.net/forum?id=UvDsWevxUI}
}

📖 Credits

This repository is based on the following repositories:

• jax_muzero
• Haiku
• Mctx
• Flax
• Monte Carlo Tree Search With Iteratively Refining State Abstractions, NeurIPS 2021, Sokota et al.