Multi-agent reinforcement learning algorithm to handle beliefs of other agents' policies and embedded beliefs

Authors:
Takaki Makino;Kazuyuki Aihara
Affiliations:
The University of Tokyo, Kashiwa, Chiba, Japan;The University of Tokyo, Komaba, Meguro-ku, Tokyo, Japan and ERATO Aihara Complexity Modelling Project, Uehara, Shibuya-ku, Tokyo, Japan
Venue:
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Year:
2006

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Abstract

We have developed a new series of multi-agent reinforcement learning algorithms that choose a policy based on beliefs about co-players' policies. The algorithms are applicable to situations where a state is fully observable by the agents, but there is no limit on the number of players. Some of the algorithms employ embedded beliefs to handle the cases that co-players are also choosing a policy based on their beliefs of others' policies. Simulation experiments on Iterated Prisoners' Dilemma games show that the algorithms using on policy-based belief converge to highly mutually-cooperative behavior, unlike the existing algorithms based on action-based belief.