Presentation 2004/3/12
A belief-state reinforcement learning scheme for a multi-agent card game
Hajime FUJITA, Shin ISHII,
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Abstract(in English) In this report, we deal with the card game "Hearts", an instance of decision making problems in partially observable situations. We present the state estimation method based on a sampling method, and a reinforcement learning (RL) scheme for a multi-agent environment using the state estimation method. Since there are often a lot of unobservable cards in this game, RL is dealt with in the framework of a partially observable Markov decision process (POMDP). Using pessimistic observations, the learning agent focuses on an important domain of a large state space, estimates unobservable states based on a sampling method from such a subspace, and makes a decision by predicting the environmental behavior. Simulation results show that our model-based POMDP-RL method with sampling state estimation is applicable to this realistic multi-agent problem.
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Keyword(in English) sampling method / POMDP / estimation of unobservable state variables / multi-agent / reinforcement learning (RL)
Paper # NC2003-205
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Committee NC
Conference Date 2004/3/12(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) A belief-state reinforcement learning scheme for a multi-agent card game
Sub Title (in English)
Keyword(1) sampling method
Keyword(2) POMDP
Keyword(3) estimation of unobservable state variables
Keyword(4) multi-agent
Keyword(5) reinforcement learning (RL)
1st Author's Name Hajime FUJITA
1st Author's Affiliation Nara Institute of Science and Technology()
2nd Author's Name Shin ISHII
2nd Author's Affiliation CREST, Japan Science and Technology Agency
Date 2004/3/12
Paper # NC2003-205
Volume (vol) vol.103
Number (no) 734
Page pp.pp.-
#Pages 6
Date of Issue