A Multi-agent Reinforcement Learning Method for Role Differentiation Using State Space Filters with Fluctuation Parameters
- https://doi.org/10.2991/jrnal.k.210521.002How to use a DOI?
- Reinforcement learning, role differentiation, meta-parameter, waveform changing, state space filter
Recently, there have been many studies on Multi-agent Reinforcement Learning (MARL) in which each autonomous agent obtains its own control rule by RL. Here, we hypothesize that different agents having individuality is more effective than uniform agents in terms of role differentiation in MARL. We have previously proposed a promoting method of role differentiation using a waveform changing parameter in MARL. In this paper, we confirm the effectiveness of role differentiation by introducing the waveform changing parameter into a state space filter through computational examples using “Pursuit Game” as a multi-agent task.
- © 2021 The Authors. Published by Atlantis Press B.V.
- 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 - 2021 DA - 2021/05 TI - A Multi-agent Reinforcement Learning Method for Role Differentiation Using State Space Filters with Fluctuation Parameters JO - Journal of Robotics, Networking and Artificial Life SP - 6 EP - 9 VL - 8 IS - 1 SN - 2352-6386 UR - https://doi.org/10.2991/jrnal.k.210521.002 DO - https://doi.org/10.2991/jrnal.k.210521.002 ID - Nagayoshi2021 ER -