Solving Sokoban using Hierarchical Reinforcement Learning with Landmarks

Sergey Pastukhov

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arXiv · 2504.04366

Solving Sokoban using Hierarchical Reinforcement Learning with Landmarks

Sergey Pastukhov

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Abstract

We introduce a novel hierarchical reinforcement learning (HRL) framework that performs top-down recursive planning via learned subgoals, successfully applied to the complex combinatorial puzzle game Sokoban. Our approach constructs a six-level policy hierarchy, where each higher-level policy generates subgoals for the level below. All subgoals and policies are learned end-to-end from scratch, without any domain knowledge. Our results show that the agent can generate long action sequences from a single high-level call. While prior work has explored 2-3 level hierarchies and subgoal-based planning heuristics, we demonstrate that deep recursive goal decomposition can emerge purely from learning, and that such hierarchies can scale effectively to hard puzzle domains.

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BibTeX RIS

Sergey Pastukhov. 2025-04-06. Solving Sokoban using Hierarchical Reinforcement Learning with Landmarks. https://arxiv.org/abs/2504.04366

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