Ben Zhou
2 indexed papers
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The paper proposes ReuseRL, a method that improves agent generalization in Reinforcement Learning by enforcing structural compressibility of successful agent trajectories into reusable skills.
The paper introduces new benchmarks for complex asynchronous planning and demonstrates that general constraint satisfaction formalizers (like CP-SAT) significantly outperform direct LLM planning or traditional domain-specific formalizers (like PDDL2.1) when handling large, complex, and time-sensitive tasks.
Papers
Robust Asynchronous Planning via Auto-Formalization
The paper introduces new benchmarks for complex asynchronous planning and demonstrates that general constraint satisfaction formalizers (like CP-SAT) significantly outperform direct LLM planning or tr…