Reinforcement Learning with Symbiotic Relationships for Multiagent Environments
- https://doi.org/10.2991/jrnal.2015.2.1.10How to use a DOI?
- reinforcement learning, symbiosis, multiagent system, cooperative behavior
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.
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- 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 -