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

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 5.6Download Report PDF
pursuit-evasion gamepartial observabilitydynamic programmingbelief preservationreinforcement learningreal-time pursuit strategyworst-case robustness
Abstract:

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 when the pursuers only have imperfect information about the evader's position. Although state-of-the-art reinforcement learning (RL) methods like Equilibrium Policy Generalization (EPG) and Grasper provide guidelines for learning graph neural network (GNN) policies robust to different game dynamics, they are restricted to the scenario of perfect information and do not take into account the possible case where the evader can predict the pursuers' actions. This paper introduces the first approach to worst-case robust real-time pursuit strategies (R2PS) under partial observability. We first prove that a traditional dynamic programming (DP) algorithm for solving Markov PEGs maintains optimality under the asynchronous moves by the evader. Then, we propose a belief preservation mechanism about the evader's possible positions, extending the DP pursuit strategies to a partially observable setting. Finally, we embed the belief preservation into the state-of-the-art EPG framework to finish our R2PS learning scheme, which leads to a real-time pursuer policy through cross-graph reinforcement learning against the asynchronous-move DP evasion strategies. After reinforcement learning, our policy achieves robust zero-shot generalization to unseen real-world graph structures and consistently outperforms the policy directly trained on the test graphs by the existing game RL approach.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
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Overview

Overall Novelty Assessment

The paper contributes a dynamic programming framework for computing worst-case robust pursuit strategies under partial observability, specifically proving that a traditional DP algorithm remains optimal when the evader moves asynchronously. It resides in the 'One-Sided Partial Observability Game Solvers' leaf, which contains only three papers total. This is a relatively sparse research direction within a taxonomy of 26 papers across the entire field, suggesting that exact algorithmic solutions for one-sided partial observability remain underexplored compared to learning-based or symmetric-information approaches.

The taxonomy reveals that neighboring leaves focus on asymmetric multi-player games, heuristic search techniques, and reinforcement learning frameworks. The paper's algorithmic foundation distinguishes it from the larger body of work in 'Reinforcement Learning for Pursuit-Evasion Under Uncertainty,' which trades formal guarantees for scalability. Its emphasis on worst-case robustness also contrasts with probabilistic methods in the 'Probabilistic and Sampling-Based Pursuit Methods' branch, which handle uncertainty through Bayesian reasoning rather than adversarial game-theoretic guarantees. The scope note for its leaf explicitly excludes learning-based and multi-agent coordination methods, clarifying that this work targets exact solvers for asymmetric information settings.

Among 12 candidates examined across three contributions, none were found to clearly refute any claim. The theoretical extension of DP to asynchronous moves examined one candidate with no refutation. The R2PS learning framework combining belief preservation with EPG examined eight candidates, again with no refutations, suggesting that the integration of belief tracking with graph neural network policies is relatively novel within the limited search scope. The empirical validation of zero-shot generalization examined three candidates without finding overlapping prior work. These statistics indicate that, within the top-12 semantic matches, the paper's specific combination of DP guarantees, belief preservation, and partial observability appears distinctive.

Based on the limited search of 12 candidates, the work appears to occupy a sparsely populated niche at the intersection of exact algorithmic methods and partial observability. The taxonomy structure confirms that most related work either pursues learning-based scalability or addresses symmetric information settings. However, the small search scope means that relevant prior work outside the top-12 semantic matches may exist, particularly in broader game theory or POMDP literature not captured by this domain-specific taxonomy.

Taxonomy

Core-task Taxonomy Papers
26
3
Claimed Contributions
12
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Computing worst-case robust pursuit strategies under partial observability in graph-based pursuit-evasion games. The field divides into several complementary branches that reflect different methodological emphases and problem settings. Algorithmic Foundations for Partially Observable Pursuit-Evasion focuses on exact or near-exact solution techniques—such as dynamic programming and value iteration—that provide formal guarantees even when one or both players have incomplete information about the game state. Reinforcement Learning for Pursuit-Evasion Under Uncertainty explores data-driven methods that learn policies from interaction, often trading theoretical guarantees for scalability and adaptability in high-dimensional or continuous domains. Probabilistic and Sampling-Based Pursuit Methods leverage stochastic reasoning and Monte Carlo techniques to handle uncertainty about evader behavior or sensor noise. Robust Optimization and Task-Oriented Navigation addresses settings where robustness to model mismatch or environmental disturbances is paramount, sometimes blending pursuit with broader navigation objectives. Finally, Institutional Research Reports and Overviews collect surveys and technical reports that synthesize progress across these areas. Within the algorithmic foundations branch, a particularly active line of work centers on one-sided partial observability game solvers, where the pursuer must reason about an evader's possible locations without full state knowledge. R2PS Robust Pursuit[0] sits squarely in this cluster, emphasizing worst-case guarantees through dynamic programming techniques akin to those in Heuristic POSG[8] and Dynamic Programming POSG[20]. These methods contrast with reinforcement learning approaches such as Online Planning DRL[3] and Observer Multi-agent MARL[4], which sacrifice formal worst-case bounds for greater flexibility in complex or continuous environments. Another interesting contrast emerges between works that assume symmetric observability—where both players face similar information constraints—and those like Asymmetric Observations Reach-Avoid[5] that explicitly model asymmetric sensing capabilities. Open questions remain about how to scale exact solvers to larger graphs and how to integrate learned heuristics without losing robustness, themes that bridge the algorithmic and learning-based branches.

Claimed Contributions

Theoretical extension of DP algorithm to asynchronous moves and partial observability

The authors prove that a traditional dynamic programming algorithm for Markov pursuit-evasion games maintains optimality when the evader moves asynchronously. They also design a belief preservation mechanism to handle partial observability, extending DP pursuit strategies to settings where pursuers have imperfect information about the evader's position.

1 retrieved paper
R2PS learning framework combining belief preservation with EPG

The authors introduce a reinforcement learning framework that embeds their belief preservation mechanism into the Equilibrium Policy Generalization (EPG) paradigm. This produces the first approach for learning worst-case robust real-time pursuit strategies under partial observability that generalize across graph structures.

8 retrieved papers
Empirical validation of zero-shot generalization under partial observability

The authors demonstrate through experiments that their approach achieves robust zero-shot generalization to unseen real-world graph structures under partial observability, consistently outperforming policies trained directly on test graphs using existing game reinforcement learning methods.

3 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Theoretical extension of DP algorithm to asynchronous moves and partial observability

The authors prove that a traditional dynamic programming algorithm for Markov pursuit-evasion games maintains optimality when the evader moves asynchronously. They also design a belief preservation mechanism to handle partial observability, extending DP pursuit strategies to settings where pursuers have imperfect information about the evader's position.

Contribution

R2PS learning framework combining belief preservation with EPG

The authors introduce a reinforcement learning framework that embeds their belief preservation mechanism into the Equilibrium Policy Generalization (EPG) paradigm. This produces the first approach for learning worst-case robust real-time pursuit strategies under partial observability that generalize across graph structures.

Contribution

Empirical validation of zero-shot generalization under partial observability

The authors demonstrate through experiments that their approach achieves robust zero-shot generalization to unseen real-world graph structures under partial observability, consistently outperforming policies trained directly on test graphs using existing game reinforcement learning methods.

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