Analysis of coordinated behavior structures with multi-agent deep reinforcement learning

Yuki Miyashita, Toshiharu Sugawara

Research output: Contribution to journalArticle

Abstract

Cooperation and coordination are major issues in studies on multi-agent systems because the entire performance of such systems is greatly affected by these activities. The issues are challenging however, because appropriate coordinated behaviors depend on not only environmental characteristics but also other agents’ strategies. On the other hand, advances in multi-agent deep reinforcement learning (MADRL) have recently attracted attention, because MADRL can considerably improve the entire performance of multi-agent systems in certain domains. The characteristics of learned coordination structures and agent’s resulting behaviors, however, have not been clarified sufficiently. Therefore, we focus here on MADRL in which agents have their own deep Q-networks (DQNs), and we analyze their coordinated behaviors and structures for the pickup and floor laying problem, which is an abstraction of our target application. In particular, we analyze the behaviors around scarce resources and long narrow passages in which conflicts such as collisions are likely to occur. We then indicated that different types of inputs to the networks exhibit similar performance but generate various coordination structures with associated behaviors, such as division of labor and a shared social norm, with no direct communication.

Original languageEnglish
JournalApplied Intelligence
DOIs
Publication statusAccepted/In press - 2020

Keywords

  • Cooperation
  • Coordination
  • Divisional cooperation
  • Multi-agent deep reinforcement learning
  • Norm

ASJC Scopus subject areas

  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Analysis of coordinated behavior structures with multi-agent deep reinforcement learning'. Together they form a unique fingerprint.

Cite this