Fast and Precise: Adjusting Planning Horizon with Adaptive Subgoal SearchDownload PDF

08 Oct 2022, 17:47 (modified: 09 Dec 2022, 14:31)Deep RL Workshop 2022Readers: Everyone
Keywords: search, adaptive horizon, verification, deep learning, hierarchical planning
TL;DR: We propose Adaptive Subgoal Search (AdaSubS), a search algorithm that adjusts the planning horizon to match the local complexity of the solved problems.
Abstract: Complex reasoning problems contain states that vary in the computational cost required to determine a good action plan. Taking advantage of this property, we propose Adaptive Subgoal Search (AdaSubS), a search method that adaptively adjusts the planning horizon. To this end, AdaSubS generates diverse sets of subgoals at different distances. A verification mechanism is employed to filter out unreachable subgoals swiftly, allowing to focus on feasible further subgoals. In this way, AdaSubS benefits from the efficiency of planning with longer subgoals and the fine control with the shorter ones, and thus scales well to difficult planning problems. We show that AdaSubS significantly surpasses hierarchical planning algorithms on three complex reasoning tasks: Sokoban, the Rubik's Cube, and inequality proving benchmark INT.
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