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Hikaru Hoshino

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Preprint Aug 2026

Physics-informed Reinforcement Learning for Stochastic Reach-Avoid Analysis

A physics-informed RL (PIRL) framework that combines the complementary strengths of PINNs and RL for stochastic reach-avoid analysis, and develops a scheduled PIRL algorithm in which temporal-difference actor-critic learning first guides the critic toward a meaningful approximation of the reach-avoid value function.

Hikaru Hoshino, Yorie Nakahira · 0 citations

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