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arXiv · 2406.00614

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

Yunhyeok Kwak·Inwoo Hwang·Dooyoung Kim·Sanghack Lee·Byoung-Tak Zhang
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Abstract

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.

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Yunhyeok Kwak, Inwoo Hwang, Dooyoung Kim, Sanghack Lee, Byoung-Tak Zhang. 2024-06-02. Efficient Monte Carlo Tree Search via On-the-Fly State-Conditioned Action Abstraction. https://arxiv.org/abs/2406.00614

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