Presentation 2001/3/16
A multi-agent reinforcement learning method with learning of other agents for competitive game
Yoichiro Matsuno, Tatsuya Yaamazaki, Jun Matsuda, Shin Ishii,
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Abstract(in English) This report proposes a reinforcement learning(RL)method based on the Actor-Critic architecture, which can be applied to partially-observable multi-agent competitive games. As an ezample, we consider a card game"Hearts". The RL then becomes a part,ally-observable Markov decision process (POMDP). In our method, a single Hearts game is divided into three stages, and three actors are prepared so that one of them plays and learns separately in each stage. In particular, the actor for the middle stage plays so as to enlarge the expected temporal-difference error, which is calculated using the evaluation function approximated by the critic and the estimated state transition. Computer experiments with heuristic players show that our RL method works well.
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Keyword(in English) Multi-agent / Reinforcement learning / Competitive game / Actor-Critic model / Opponent-agent model inference
Paper # NC2000-168
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Committee NC
Conference Date 2001/3/16(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 multi-agent reinforcement learning method with learning of other agents for competitive game
Sub Title (in English)
Keyword(1) Multi-agent
Keyword(2) Reinforcement learning
Keyword(3) Competitive game
Keyword(4) Actor-Critic model
Keyword(5) Opponent-agent model inference
1st Author's Name Yoichiro Matsuno
1st Author's Affiliation Nara Institute of Science and Technology()
2nd Author's Name Tatsuya Yaamazaki
2nd Author's Affiliation ATR Adaptive Commuinications Research Laboratories
3rd Author's Name Jun Matsuda
3rd Author's Affiliation Osaka Gakuin Univercity
4th Author's Name Shin Ishii
4th Author's Affiliation Nara Institute of Science and Technology:CREST, Japan Science and Technology Corporation
Date 2001/3/16
Paper # NC2000-168
Volume (vol) vol.100
Number (no) 688
Page pp.pp.-
#Pages 8
Date of Issue