Efficient Monte Carlo Tree Search via On-the-Fly State-Conditioned Action Abstraction
<|reference_start|>Efficient Monte Carlo Tree Search via On-the-Fly State-Conditioned Action Abstraction: Monte Carlo Tree Search (MCTS) has showcased its efficacy across a broad spectrum of decision-making problems. However, its performance often degrades under vast combinatorial action space, especially where an action is composed of multiple sub-actions. In this work, we propose an action abstraction based on the compositional structure between a state and sub-actions for improving the efficiency of MCTS under a factored action space. Our method learns a latent dynamics model with an auxiliary network that captures sub-actions relevant to the transition on the current state, which we call state-conditioned action abstraction. Notably, it infers such compositional relationships from high-dimensional observations without the known environment model. During the tree traversal, our method constructs the state-conditioned action abstraction for each node on-the-fly, reducing the search space by discarding the exploration of redundant sub-actions. Experimental results demonstrate the superior sample efficiency of our method compared to vanilla MuZero, which suffers from expansive action space.<|reference_end|>
@article{kwak2024efficient,
    title={Efficient Monte Carlo Tree Search via On-the-Fly State-Conditioned
  Action Abstraction},
    author={Yunhyeok Kwak, Inwoo Hwang, Dooyoung Kim, Sanghack Lee, Byoung-Tak
  Zhang},
    journal={arXiv preprint arXiv:2406.00614},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.00614},
    primaryClass={cs.LG cs.AI}
}