Multiple Pursuer Multiple Evader Differential Games
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Abstract (arXiv)
In this paper an N-pursuer vs. M-evader team conflict is studied. The differential game of border defense is addressed and we focus on the game of degree in the region of the state space where the pursuers are able to win. This work extends classical differential game theory to simultaneously address weapon assignments and multi-player pursuit-evasion scenarios. Saddle-point strategies that provide guaranteed performance for each team regardless of the actual strategies implemented by the opponent are devised. The players' optimal strategies require the co-design of cooperative optimal assignments and optimal guidance laws. A representative measure of performance is proposed and the Value function of the game is obtained. It is shown that the Value function is continuous, continuously differentiable, and that it satisfies the Hamilton-Jacobi-Isaacs equation - the curse of dimensionality is overcome and the optimal strategies are obtained. The cases of N=M and N>M are considered. In the latter case, cooperative guidance strategies are also developed in order for the pursuers to exploit their numerical advantage. This work provides a foundation to formally analyze complex and high-dimensional conflicts between teams of N pursuers and M evaders by means of differential game theory.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Nov 2019.
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Citing papers per year: 2022: 2; 2023: 1; 2024: 0; 2025: 1; 2026: 1.
5 papers.
- 2022 T3 OMVP: A Transformer-based Time and Team Reinforcement Learning Scheme for Observation-constrained Multi-Vehicle Pursuit in Urban Areacited by 5
- 2022A Survey of Decision Making in Adversarial Gamescited by 3
- 2023Progression Cognition Reinforcement Learning with Prioritized Experience for Multi-Vehicle Pursuitcited by 0
- 2025A Three-Dimensional Pursuit-Evasion Game Based on Fuzzy Actor-Critic Learning Algorithmcited by 0
- 2026Game-Theory-Assisted Reinforcement Learning for Border Defense: Early Termination based on Analytical Solutionscited by 0
- 2026Game-Theory-Assisted Reinforcement Learning for Border Defense: Early Termination based on Analytical Solutions16 Mar 2026
- 2025A Three-Dimensional Pursuit-Evasion Game Based on Fuzzy Actor-Critic Learning Algorithm11 Mar 2025
- 2023Progression Cognition Reinforcement Learning with Prioritized Experience for Multi-Vehicle Pursuit8 Jun 2023
- 2022A Survey of Decision Making in Adversarial Games16 Jul 2022
- 2022 T3 OMVP: A Transformer-based Time and Team Reinforcement Learning Scheme for Observation-constrained Multi-Vehicle Pursuit in Urban Area1 Mar 2022