  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.
