Strategy for learning cooperative behavior with local information for multi-agent systems

Fumito Uwano, Keiki Takadama

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

1 Citation (Scopus)

Abstract

Toward learning cooperative behavior for any number of agents, this paper proposes a multi-agent reinforcement learning method without communication, called PMRL-based Learning for Any number of Agents (PLAA). PLAA prevents from agents reaching the purpose for spending too many times, and to promote the local multi-agent cooperation without communication by PMRL as a previous method. To guarantee the effectiveness of PLAA, this paper compares PLAA with Q-learning, and two previous methods in 10 kinds of the maze for the 2 and 3 agents. From the experimental result, we revealed those things: (a) PLAA is the most effective method for cooperation among 2 and 3 agents; (b) PLAA enable the agents to cooperate with each other in small iterations.

Original languageEnglish
Title of host publicationPRIMA 2018
Subtitle of host publicationPrinciples and Practice of Multi-Agent Systems - 21st International Conference, 2018, Proceedings
EditorsNir Oren, Yuko Sakurai, Itsuki Noda, Tran Cao Son, Tim Miller, Bastin Tony Savarimuthu
PublisherSpringer Verlag
Pages663-670
Number of pages8
ISBN (Print)9783030030971
DOIs
Publication statusPublished - 2018
Externally publishedYes
Event21st International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2018 - Tokyo, Japan
Duration: Oct 29 2018Nov 2 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11224 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference21st International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2018
CountryJapan
CityTokyo
Period10/29/1811/2/18

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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