Analysis of Coordination Structures of Partially Observing Cooperative Agents by Multi-agent Deep Q-Learning

Ken Smith, Yuki Miyashita, Toshiharu Sugawara

研究成果: Conference contribution

抄録

We compare the coordination structures of agents using different types of inputs for their deep Q-networks (DQNs) by having agents play a distributed task execution game. The efficiency and performance of many multi-agent systems can be significantly affected by the coordination structures formed by agents. One important factor that may affect these structures is the information provided to an agent’s DQN. In this study, we analyze the differences in coordination structures in an environment involving walls to obstruct visibility and movement. Additionally, we introduce a new DQN input, which performs better than past inputs in a dynamic setting. Experimental results show that agents with their absolute locations in their DQN input indicate a granular level of labor division in some settings, and that the consistency of the starting locations of agents significantly affects the coordination structures and performances of agents.

本文言語English
ホスト出版物のタイトルPRIMA 2020
ホスト出版物のサブタイトルPrinciples and Practice of Multi-Agent Systems - 23rd International Conference, 2020, Proceedings
編集者Takahiro Uchiya, Quan Bai, Iván Marsá Maestre
出版社Springer Science and Business Media Deutschland GmbH
ページ150-164
ページ数15
ISBN(印刷版)9783030693213
DOI
出版ステータスPublished - 2021
イベント23rd International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2020 - Virtual, Online
継続期間: 2020 11 182020 11 20

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
12568 LNAI
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference23rd International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2020
CityVirtual, Online
Period20/11/1820/11/20

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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