• Journal of Internet Computing and Services
    ISSN 2287 - 1136(Online) / ISSN 1598 - 0170 (Print)
    http://jics.or.kr/

A Naive Bayesian-based Model of the Opponent's Policy for Efficient Multiagent Reinforcement Learning


Ki-Duk Kwon, Journal of Internet Computing and Services, Vol. 9, No. 6, pp. 165-178, Dec. 2008
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Keywords: Multiagent, Reinforcement Learning, Naive Bayesian

Abstract

An important issue in Multiagent reinforcement learning is how an agent should learn its optimal policy in a dynamic environment where there exist other agents able to influence its own performance. Most previous works for Multiagent reinforcement learning tend to apply single-agent reinforcement learning techniques without any extensions or require some unrealistic assumptions even though they use explicit models of other agents. In this paper, a Naive Bayesian based policy model of the opponent agent is introduced and then the Multiagent reinforcement learning method using this model is explained. Unlike previous works, the proposed Multiagent reinforcement learning method utilizes the Naive Bayesian based policy model, not the Q function model of the opponent agent. Moreover, this learning method can improve learning efficiency by using a simpler one than other richer but time-consuming policy models such as Finite State Machines(FSM) and Markov chains. In this paper, the Cat and Mouse game is introduced as an adversarial Multiagent environment. And then effectiveness of the proposed Naive Bayesian based policy model is analyzed through experiments using this game as test-bed.


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Cite this article
[APA Style]
Ki-Duk Kwon (2008). A Naive Bayesian-based Model of the Opponent's Policy for Efficient Multiagent Reinforcement Learning. Journal of Internet Computing and Services, 9(6), 165-178.

[IEEE Style]
K. Kwon, "A Naive Bayesian-based Model of the Opponent's Policy for Efficient Multiagent Reinforcement Learning," Journal of Internet Computing and Services, vol. 9, no. 6, pp. 165-178, 2008.

[ACM Style]
Ki-Duk Kwon. 2008. A Naive Bayesian-based Model of the Opponent's Policy for Efficient Multiagent Reinforcement Learning. Journal of Internet Computing and Services, 9, 6, (2008), 165-178.