This article has been published in Autonomous Agents and Multi-Agent Systems (AAMAS 2019).
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.
- Title: The impact of agent definitions and interactions in multiagent learning for coordination in traffic management domains
- Authors: Chung, Jen Jen; Miklic, Damjan; Sabattini, Lorenzo; Tumer, Kagan; Siegwart, Roland
- Date of publication: 23/01/2019
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