Ruochuan Shi — accepted AI-conference papers

2026

R2PS: Worst-Case Robust Real-Time Pursuit Strategies under Partial Observability

Runyu Lu, Ruochuan Shi, Yuanheng Zhu, Dongbin Zhao

ICLR 2026poster

Computing worst-case robust strategies in pursuit-evasion games (PEGs) is time-consuming, especially when real-world factors like partial observability are considered. While important for general security purposes, real-time applicable pursuit strategies for graph-based PEGs are currently missing wh…

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2025

Equilibrium Policy Generalization: A Reinforcement Learning Framework for Cross-Graph Zero-Shot Generalization in Pursuit-Evasion Games

Runyu Lu, Peng Zhang, Ruochuan Shi, Yuanheng Zhu, Dongbin Zhao, Yang Liu +2

NeurIPS 2025poster

Equilibrium learning in adversarial games is an important topic widely examined in the fields of game theory and reinforcement learning (RL). Pursuit-evasion game (PEG), as an important class of real-world games from the fields of robotics and security, requires exponential time to be accurately sol…

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