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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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 -