mlsanityData to 14 Oct 2026

Online Competitive Information Gathering for Partially Observable Trajectory Games

arXiv 2506.01927 ↗First posted 2 Jun 2025cs.GT cs.AI cs.MAcited by 1

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Abstract (arXiv)

Game-theoretic agents must make plans that optimally gather information about their opponents. These problems are modeled by partially observable stochastic games (POSGs), but planning in fully continuous POSGs is intractable without heavy offline computation or assumptions on the order of belief maintained by each player. We formulate a finite history/horizon refinement of POSGs which admits competitive information gathering behavior in trajectory space, and through a series of approximations, we present an online method for computing rational trajectory plans in these games which leverages particle-based estimations of the joint state space and performs stochastic gradient play. We also provide the necessary adjustments required to deploy this method on individual agents. The method is tested in continuous pursuit-evasion and warehouse-pickup scenarios (alongside extensions to N > 2 players and to more complex environments with visual and physical obstacles), demonstrating evidence of active information gathering and outperforming passive competitors.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 12 of its 27 references that are in the map. First posted on arXiv 2 Jun 2025.

Builds on

12 of 27 references.

References · 12

  1. 2017A Unified Game-Theoretic Approach to Multiagent Reinforcement Learningcited by 268
  2. 2016Deep Reinforcement Learning from Self-Play in Imperfect-Information Gamescited by 158
  3. 2022Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learningcited by 131
  4. 2018Deep Counterfactual Regret Minimizationcited by 103
  5. 2018On Gradient-Based Learning in Continuous Gamescited by 48
  6. 2022Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and Planningcited by 48
  7. 2019Rethinking Formal Models of Partially Observable Multiagent Decision Makingcited by 36
  8. 2021XDO: A Double Oracle Algorithm for Extensive-Form Gamescited by 34
  9. 2019Stochastic Dynamic Games in Belief Spacecited by 23
  10. 2019ALGAMES: A Fast Solver for Constrained Dynamic Gamescited by 9
  11. 2022Learning Mixed Strategies in Trajectory Gamescited by 4
  12. 2024Bridging the Gap between Partially Observable Stochastic Games and Sparse POMDP Methodscited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Generalized Information Gathering Under Dynamics Uncertaintycited by 0