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A reinforcement learning scheme for a multi-agent card game

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3 Author(s)
Fujita, H. ; Nara Inst. of Sci. & Technol., Japan ; Matsuno, Y. ; Ishii, S.

We formulate an automatic strategy acquisition problem for the multi-agent card game "hearts" as a reinforcement learning (RL) problem. Since there are often a lot of unobservable cards in this game, RL is approximately dealt with in the framework of a partially observable Markov decision process (POMDP). This article presents a POMDP-RL method based on estimation of unobservable state variables and prediction of actions of the opponent agents. Simulation results show our model-based POMDP-RL method is applicable to a realistic multi-agent problem.

Published in:

Systems, Man and Cybernetics, 2003. IEEE International Conference on  (Volume:5 )

Date of Conference:

5-8 Oct. 2003

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