Reinforcement Learning with Symbiotic Relationships for Multiagent Environments

Shingo Mabu, Masanao Obayashi, Takashi Kuremoto
Corresponding Author
Shingo Mabu
Available Online 1 June 2015.
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.

© 2013, the Authors. Published by ALife Robotics Corp. Ltd.
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