Structure-Induced Information for Rerooting Levin Tree Search
Quick Take
The paper introduces a learned 'rerooter' for subgoal-based policy tree search using the $ ext{LTS}$ algorithm, enabling scalable problem decomposition without explicit subgoal generation. Three designs are proposed: clustering-based, heuristic-based, and a hybrid approach, achieving state-of-the-art efficiency in complex environments where traditional methods fail.
Key Points
- Introduces a learned 'rerooter' to enhance scalability in policy tree search.
- Proposes three rerooter designs: clustering-based, heuristic-based, and hybrid.
- Avoids explicit subgoal generation, reducing computational overhead significantly.
- Achieves state-of-the-art online training efficiency in tested complex environments.
- Empirically scales to environments where traditional subgoal-based methods fail.
Article Content
From source RSS / original summaryarXiv:2605. 30664v1 Announce Type: new Abstract: 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 limitations by using a learned ``rerooter'' through the recently-introduced $\sqrt{\text{LTS}}$ algorithm. A rerooter implicitly decomposes the problem into soft subtasks.
While previous work focused on the formal guarantees for given or handcrafted rerooters, in this work we propose three rerooter designs: (i) a clustering-based rerooter that exploits global state-space structure, (ii) a heuristic-based rerooter that leverages learned cost-to-go estimates, and (iii) a hybrid that combines both signals.
Our framework avoids having to explicitly reconstruct and reason over generated subgoals, thereby enabling scalable allocation of search effort with significantly lower computational overhead. Empirically, our rerooting-based methods scale to complex environments where subgoal-based policy tree search fails, and achieve state-of-the-art online training efficiency on the domains tested.
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