The Representational Capacity of Action-Value Networks for Multi-Agent Reinforcement Learning

Jacopo Castellini, Frans A. Oliehoek, Rahul Savani, Shimon Whiteson

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

13 Citations (Scopus)
27 Downloads (Pure)

Abstract

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 publication18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019
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 (Electronic)9781510892002
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

Publication series

NameProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Volume4
ISSN (Print)1548-8403
ISSN (Electronic)1558-2914

Conference

ConferenceAAMAS 2019
Country/TerritoryCanada
CityMontreal
Period13/05/1917/05/19

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
 
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Actionvalue representation
  • Decision-making
  • Multi-agent systems
  • Neural networks
  • One-shot games

Fingerprint

Dive into the research topics of 'The Representational Capacity of Action-Value Networks for Multi-Agent Reinforcement Learning'. Together they form a unique fingerprint.

Cite this