R2PS combines a proof that dynamic programming remains optimal under asynchronous evader moves, a belief preservation mechanism for partial observability, and integration into equilibrium policy generalization to produce real-time pursuer policies that zero-shot generalize to unseen graphs.
(Lu et al., 2025a) shows that an expansion-based dynamic programming algorithm can solve Markov PEGs under a near-optimal time complexity
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R2PS: Worst-Case Robust Real-Time Pursuit Strategies under Partial Observability
R2PS combines a proof that dynamic programming remains optimal under asynchronous evader moves, a belief preservation mechanism for partial observability, and integration into equilibrium policy generalization to produce real-time pursuer policies that zero-shot generalize to unseen graphs.