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This paper presents a novel framework for synthesizing finite-state controllers for Partially Observable Markov Decision Processes (POMDPs) by integrating sampling, automata learning, and model-checking. The approach provides formal guarantees for threshold-safety problems that elude existing formal synthesis tools.
This paper introduces LANTERN, a framework for multi-source neurosymbolic transfer in reinforcement learning that uses LLMs to generate task automata and adaptive gating to improve sample efficiency.