Structure-Induced Information for Rerooting Levin Tree Search
Innovating search algorithms by replacing manual subgoal generation with learned task decomposition.
Subgoal-based policy tree search, which uses a policy to guide search, is effective for complex single-agent deterministic problems but often relies on explicit subgoal generation that can incur substantial overhead and hinders scalability. In this paper, we overcome these limita...
ArXiv CS.AI
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This research introduces a novel rerooting mechanism that improves search efficiency by implicitly decomposing complex problems without the overhead of explicit subgoal generation. By scaling search efforts through learned structural insights, this approach sets a new standard for performance in challenging single-agent environments.
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