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Recent years have seen the application of deep reinforcement learning techniques to cooperative multi-agent systems, with great empirical success. In this work, we empirically investigate the representational power of various network architectures on a series of one-shot games. Despite their simplicity, these games capture many of the crucial problems that arise in the multi-agent setting, such as an exponential number of joint actions or the lack of an explicit coordination mechanism. Our results quantify how well various approaches can represent the requisite value functions, and help us identify issues that can impede good performance.
Original languageEnglish
Title of host publicationAAMAS'19
Subtitle of host publicationProceedings of the Eighteenth International Conference on Autonomous Agents and Multiagent Systems (AAMAS)
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages1862-1864
Number of pages3
ISBN (Print)978-1-4503-6309-9
Publication statusPublished - 2019
EventAAMAS 2019: The 18th International Conference on Autonomous Agents and MultiAgent Systems - Montreal, Canada
Duration: 13 May 201917 May 2019

Conference

ConferenceAAMAS 2019
CountryCanada
CityMontreal
Period13/05/1917/05/19

    Research areas

  • multi-agent systems, neural networks, decision-making, actionvaluerepresentation, one-shotgames

ID: 67209027