Journal of Robotics, Networking and Artificial Life

Volume 6, Issue 4, March 2020, Pages 221 - 224

A Promoting Method of Role Differentiation using a Learning Rate that has a Periodically Negative Value in Multi-agent Reinforcement Learning

Authors
Masato Nagayoshi1, *, Simon J. H. Elderton1, Hisashi Tamaki2
1Department of Nursing, Niigata College of Nursing, 240 Shinnan-cho, Joetsu, Niigata 943-0147, Japan
2Department of Computer Science and System Engineering, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe, Hyogo 657-8501, Japan
*Corresponding author. Email: nagayosi@niigata-cn.ac.jp
Corresponding Author
Masato Nagayoshi
Received 31 October 2019, Accepted 16 December 2019, Available Online 28 February 2020.
DOI
10.2991/jrnal.k.200222.003How to use a DOI?
Keywords
Reinforcement learning; multi-agent; negative learning rate; role differentiation
Abstract

There have been many studies on Multi-Agent Reinforcement Learning (MARL) in which each autonomous agent obtains its own control rule by Reinforcement Learning (RL). Here, we hypothesize that different agents having individuality is more effective than uniform agents in terms of role differentiation in MARL. In this paper, we propose a promoting method of role differentiation using a wave-form changing parameter in MARL. Then we confirm the effectiveness of role differentiation by the learning rate that has a periodically negative value through computational experiments.

Copyright
© 2020 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
Journal of Robotics, Networking and Artificial Life
Volume-Issue
6 - 4
Pages
221 - 224
Publication Date
2020/02/28
ISSN (Online)
2352-6386
ISSN (Print)
2405-9021
DOI
10.2991/jrnal.k.200222.003How to use a DOI?
Copyright
© 2020 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Masato Nagayoshi
AU  - Simon J. H. Elderton
AU  - Hisashi Tamaki
PY  - 2020
DA  - 2020/02/28
TI  - A Promoting Method of Role Differentiation using a Learning Rate that has a Periodically Negative Value in Multi-agent Reinforcement Learning
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 221
EP  - 224
VL  - 6
IS  - 4
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.k.200222.003
DO  - 10.2991/jrnal.k.200222.003
ID  - Nagayoshi2020
ER  -