# Online Competitive Information Gathering for Partially Observable Trajectory Games

arXiv: https://arxiv.org/abs/2506.01927 · HTML: https://mlsanity.com/paper/2506.01927/

## Key facts

- Cited by 1 papers 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 (most cited first, 12 of 12)

- [A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning](https://mlsanity.com/paper/1711.00832/) (2017)
- [Deep Reinforcement Learning from Self-Play in Imperfect-Information Games](https://mlsanity.com/paper/1603.01121/) (2016)
- [Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning](https://mlsanity.com/paper/2206.15378/) (2022)
- [Deep Counterfactual Regret Minimization](https://mlsanity.com/paper/1811.00164/) (2018)
- [On Gradient-Based Learning in Continuous Games](https://mlsanity.com/paper/1804.05464/) (2018)
- [Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and Planning](https://mlsanity.com/paper/2210.05492/) (2022)
- [Rethinking Formal Models of Partially Observable Multiagent Decision Making](https://mlsanity.com/paper/1906.11110/) (2019)
- [XDO: A Double Oracle Algorithm for Extensive-Form Games](https://mlsanity.com/paper/2103.06426/) (2021)
- [Stochastic Dynamic Games in Belief Space](https://mlsanity.com/paper/1909.06963/) (2019)
- [ALGAMES: A Fast Solver for Constrained Dynamic Games](https://mlsanity.com/paper/1910.09713/) (2019)
- [Learning Mixed Strategies in Trajectory Games](https://mlsanity.com/paper/2205.00291/) (2022)
- [Bridging the Gap between Partially Observable Stochastic Games and Sparse POMDP Methods](https://mlsanity.com/paper/2405.18703/) (2024)

## Built on by (most cited first, 1 of 1)

- [Generalized Information Gathering Under Dynamics Uncertainty](https://mlsanity.com/paper/2601.21988/) (2026)

## 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.