Journal of Robotics, Networking and Artificial Life

Volume 2, Issue 1, June 2015, Pages 40 - 45

Reinforcement Learning with Symbiotic Relationships for Multiagent Environments

Authors
Shingo Mabu, Masanao Obayashi, Takashi Kuremoto
Corresponding Author
Shingo Mabu
Available Online 1 June 2015.
DOI
https://doi.org/10.2991/jrnal.2015.2.1.10How to use a DOI?
Keywords
reinforcement learning, symbiosis, multiagent system, cooperative behavior
Abstract

Multiagent systems, where many agents work together to achieve their objectives, and cooperative behaviors between agents need to be realized, have been widely studied In this paper, a new reinforcement learning framework considering the concept of “Symbiosis” in order to represent complicated relationships between agents and analyze the emerging behavior is proposed. In addition, distributed state-action value tables are designed to efficiently solve the multiagent problems with large number of state-action pairs. From the simulation results, it is clarified that the proposed method shows better performance comparing to the conventional reinforcement learning without considering symbiosis.

Copyright
© 2013, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
Journal of Robotics, Networking and Artificial Life
Volume-Issue
2 - 1
Pages
40 - 45
Publication Date
2015/06/01
ISSN (Online)
2352-6386
ISSN (Print)
2405-9021
DOI
https://doi.org/10.2991/jrnal.2015.2.1.10How to use a DOI?
Copyright
© 2013, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Shingo Mabu
AU  - Masanao Obayashi
AU  - Takashi Kuremoto
PY  - 2015
DA  - 2015/06/01
TI  - Reinforcement Learning with Symbiotic Relationships for Multiagent Environments
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 40
EP  - 45
VL  - 2
IS  - 1
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.2015.2.1.10
DO  - https://doi.org/10.2991/jrnal.2015.2.1.10
ID  - Mabu2015
ER  -