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.

基于组合结构的行为抽象可以提高蒙特卡洛树搜索在具有复杂动作空间问题上的效率。本研究通过学习一个潜在的动态模型和辅助网络，构建了一种状态条件行为抽象方法，用于减少搜索空间并提高采样效率，并在实验中展示了其比传统 MuZero 方法更好的采样效率。