Reinforcement Learning with Symbiotic Relationships for Multiagent Environments

DOI

10.2991/jrnal.2015.2.1.10How to use a DOI?

Keywords

reinforcement learning, symbiosis, multiagent system, cooperative behavior

Abstract

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.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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  - 10.2991/jrnal.2015.2.1.10
ID  - Mabu2015
ER  -