Multi-Agent Coordination Profiles through State Space Perturbations

Derrik E. Asher, Michael Garber-Barron, Sebastian S. Rodriguez, Erin G. Zaroukian, Nicholas R. Waytowich
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引用次数: 6

Abstract

The current work utilized a multi-agent reinforcement learning (MARL) algorithm embedded in a continuous predator-prey pursuit simulation environment to measure and evaluate coordination between cooperating agents. In this simulation environment, it is generally assumed that successful performance for cooperative agents necessarily results in the emergence of coordination, but a clear quantitative demonstration of coordination in this environment still does not exist. The current work focuses on 1) detecting emergent coordination between cooperating agents in a multi-agent predator-prey simulation environment, and 2) showing coordination profiles between cooperating agents extracted from systematic state perturbations. This work introduces a method for detecting and comparing the typically 'black-box' behavioral solutions that result from emergent coordination in multi-agent learning spatial tasks with a shared goal. Comparing coordination profiles can provide insights into overlapping patterns that define how agents learn to interact in cooperative multi-agent environments. Similarly, this approach provides an avenue for measuring and training agents to coordinate with humans. In this way, the present work looks towards understanding and creating artificial team-mates that will strive to coordinate optimally.
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状态空间扰动下的多智能体协调曲线
目前的工作利用嵌入在连续捕食者-猎物追逐模拟环境中的多智能体强化学习(MARL)算法来测量和评估合作智能体之间的协调。在这种仿真环境中,一般认为协作agent的成功执行必然会导致协调的出现,但在这种环境下,仍然没有一个明确的定量的协调论证。目前的工作重点是1)在多智能体捕食-猎物模拟环境中检测合作智能体之间的紧急协调;2)从系统状态扰动中提取合作智能体之间的协调特征。这项工作介绍了一种检测和比较典型的“黑箱”行为解决方案的方法,这些解决方案是由具有共同目标的多智能体学习空间任务中的紧急协调产生的。比较协调配置文件可以提供对重叠模式的洞察,这些模式定义了代理如何在合作多代理环境中学习交互。类似地,这种方法为测量和训练代理与人类协调提供了途径。通过这种方式,目前的工作旨在理解和创造人工团队成员,以努力实现最佳协调。
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