想弄明白为啥机器人从简单奖励就能自动集群？这篇用ARM工具看到机器人内部学到的几何场，合作和对抗场景都适用。
论文提出EEC解释框架，包含新分析工具Agent Response Map (ARM)。ARM揭示机器人隐含学习环境几何场并用作协调运动目标。在合作的多机器人形状组装任务中，ARM发现机器人将未占用的目标内部作为导航目的地区域，随中心被占自动移向边界。在竞争的捕食者-猎物追击任务中，ARM识别捕食者Voronoi图边界作为猎物汇聚目的地。
Unveiling Complex Collective Behaviors from Simple Rewards
Multi-agent Reinforcement Learning (MARL) holds great potential for robot swarms, but the black-box nature of neural policies complicates strategic analysis, limiting multi-robot applications. Furthermore, complex swarm behaviors can surprisingly emerge from simple rewards without explicit aggregation incentives. Unveiling the mechanisms behind this emergence is critical, but the disconnection between simple rewards and collective behaviors exacerbates interpretability challenges. This paper aims to reveal the hidden mechanisms in this process. We propose a two-stage EEC (\LinkIII) explanatory framework. This includes a novel analytical tool called the Agent Response Map (ARM), which reveals agents' decision-making patterns across space and identifies regions of aggregation and avoidance. ARM reveals that the robots implicitly learn the geometric fields of the environment and utilize these structures as desired targets for coordinated movement. We validate this finding across two distinct tasks: a cooperative multi-robot shape assembly and a competitive predator-prey pursuit-evasion. 1) In the cooperative task, ARM identifies the unoccupied target interior as the desired destination for robot navigation. As the center becomes occupied, this target region automatically shifts toward the boundary, demonstrating the robots' capacity to autonomously explore unoccupied areas. 2) In the competitive task, ARM surprisingly identifies the boundary of the predators' Voronoi diagram as the convergence destination for prey agents. Together, these two tasks demonstrate the capability of ARM to discover the hidden geometric structures underlying MARL policies in robot swarms.