The state-action space of an individual agent in a multiagent team fundamentally dictates how the individual interacts with the rest of the team. Thus, how an agent is defined in the context of its domain has a significant effect on team performance when learning to coordinate. In this work we explore the trade-offs associated with these design choices, for example, having fewer agents in the team that individually are able to process and act on a wider scope of information about the world versus a larger team of agents where each agent observes and acts in a more local region of the domain. We focus our study on a traffic management domain and highlight the trends in learning performance when applying different agent definitions. In addition, we analyze the impact of agent failure for different agent definitions and investigate the ability of the team to learn new coordination strategies when individual agents become unresponsive.

中文翻译：

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代理定义和交互对多代理学习的影响，以在流量管理领域进行协调

多代理团队中单个代理的状态行动空间从根本上决定了个人如何与团队其他成员进行交互。因此，在学习协调时，如何在其领域的上下文中定义代理对团队绩效有重大影响。在这项工作中，我们探索与这些设计选择相关的取舍，例如，团队中的代理商较少，每个代理商能够单独处理和处理有关世界的更广泛的信息，而更大的代理商团队则需要每个代理商在域的较本地区域中观察并执行操作。我们将研究重点放在流量管理领域，并强调在应用不同的代理定义时学习性能的趋势。此外，