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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.
@InProceedings{tumer-chung_aamas19, author = {J. J. Chung and D. Miklic and L. Sabattini and K. Tumer and R. Siegwart}, title = {The Impact of Agent Definitions and Interactions on Multiagent Learning for Coordination}, booktitle = {Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems}, address = {Montreal, Canada}, month = {May}, pages={}, abstract={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.}, bib2html_pubtype = {Refereed Conference Papers}, bib2html_rescat = {Multiagent Systems}, year = {2019} }
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