Coordination in Collaborative Work by Deep Reinforcement Learning with Various State Descriptions

Yuki Miyashita, Toshiharu Sugawara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Cooperation and coordination are sophisticated behaviors and are still major issues in studies on multi-agent systems because how to cooperate and coordinate depends on not only environmental characteristics but also the behaviors/strategies that closely affect each other. On the other hand, recently using the multi-agent deep reinforcement learning (MADRL) has received much attention because of the possibility of learning and facilitating their coordinated behaviors. However, the characteristics of socially learned coordination structures have been not sufficiently clarified. In this paper, by focusing on the MADRL in which each agent has its own deep Q-networks (DQNs), we show that the different types of input to the network lead to various coordination structures, using the pickup and floor laying problem, which is an abstract form related to our target problem. We also indicate that the generated coordination structures affect the entire performance of multi-agent systems.

Original languageEnglish
Title of host publicationPRIMA 2019
Subtitle of host publicationPrinciples and Practice of Multi-Agent Systems - 22nd International Conference, Proceedings
EditorsMatteo Baldoni, Mehdi Dastani, Beishui Liao, Yuko Sakurai, Rym Zalila Wenkstern
PublisherSpringer
Pages550-558
Number of pages9
ISBN (Print)9783030337919
DOIs
Publication statusPublished - 2019
Event22nd International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2019 - Turin, Italy
Duration: 2019 Oct 282019 Oct 31

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11873 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2019
CountryItaly
CityTurin
Period19/10/2819/10/31

Keywords

  • Cooperation
  • Coordination
  • Deep Q networks
  • Divisional cooperation
  • Multi-agent deep reinforcement learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Miyashita, Y., & Sugawara, T. (2019). Coordination in Collaborative Work by Deep Reinforcement Learning with Various State Descriptions. In M. Baldoni, M. Dastani, B. Liao, Y. Sakurai, & R. Zalila Wenkstern (Eds.), PRIMA 2019: Principles and Practice of Multi-Agent Systems - 22nd International Conference, Proceedings (pp. 550-558). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11873 LNAI). Springer. https://doi.org/10.1007/978-3-030-33792-6_40