# Cooperative Team Strategies for Multi-player Perimeter-Defense Games

arXiv: https://arxiv.org/abs/1912.04342 · HTML: https://mlsanity.com/paper/1912.04342/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2019.

## Built on by (most cited first, 6 of 6)

- [Neurosymbolic Transformers for Multi-Agent Communication](https://mlsanity.com/paper/2101.03238/) (2021)
- [Vision-based Perimeter Defense via Multiview Pose Estimation](https://mlsanity.com/paper/2209.12136/) (2022)
- [A Decentralized Spike-based Learning Framework for Sequential Capture in Discrete Perimeter Defense Problem](https://mlsanity.com/paper/2305.16748/) (2023)
- [Embedded Mean Field Reinforcement Learning for Perimeter-defense Game](https://mlsanity.com/paper/2505.14209/) (2025)
- [Strategic Communication under Threat: Learning Information Trade-offs in Pursuit-Evasion Games](https://mlsanity.com/paper/2510.07813/) (2025)
- [Game-Theory-Assisted Reinforcement Learning for Border Defense: Early Termination based on Analytical Solutions](https://mlsanity.com/paper/2603.15907/) (2026)

## Abstract (arXiv)

This paper studies a variant of the multi-player reach-avoid game played between intruders and defenders with applications to perimeter defense. The intruder team tries to score by sending as many intruders as possible to the target area, while the defender team tries to minimize this score by intercepting them. Finding the optimal strategies of the game is challenging due to the high dimensionality of the joint state space, and the existing works have proposed approximation methods to reduce the design of the defense strategy into assignment problems. However they suffer from either suboptimal defender performance or computational complexity. Based on a novel decomposition method, this paper proposes a scalable (polynomial-time) assignment algorithm that accommodates cooperative behaviors and outperforms the existing defense strategies. For a certain class of initial configurations, we derive the exact score by showing that the lower bound provided by the intruder team matches the upper bound provided by the defender team, which also proves the optimality of the team strategies.