This article has been published in the 18th International Conference on Autonomous Agents and MultiAgent Systems.
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.
- Title: The Impact of Agent Definitions and Interactions on Multiagent Learning for Coordination
- Authors: Chung, Jen Jen; Miklić, Damjan; Sabattini, Lorenzo; Tumer, Kagan; Siegwart, Roland
- Date of publication: 8 Mai 2019
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