Presentation 2001/7/16
Multi-agent reinforcement learning method for Markov games : An approach based on the estimation of the environmental model
Yasuo Nagayuki, Minoru Ito,
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Abstract(in English) In this article, we propose a multi-agent reinforcement learning method for Markov games. In our multi-agent reinforcement learning method, each agent infers the environmental model which consists of the other agents' policies and the state transition function, and estimates the future states by using the inferred environmental model. Each agent conducts its reinforcement learning based on the estimated future states. In order to evaluate our multi-agent reinforcement learning method, we employ the variant of the pursuit problem as a task. Through experiments, we demonstrate that our multi-agent reinforcement learning method is effective.
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Keyword(in English) multi-agent reinforcement learning / TD learning / environmental model / Markov game / pursuit problem
Paper # OFS2001-10,AI2001-15
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Committee AI
Conference Date 2001/7/16(1days)
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Registration To Artificial Intelligence and Knowledge-Based Processing (AI)
Language JPN
Title (in Japanese) (See Japanese page)
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Title (in English) Multi-agent reinforcement learning method for Markov games : An approach based on the estimation of the environmental model
Sub Title (in English)
Keyword(1) multi-agent reinforcement learning
Keyword(2) TD learning
Keyword(3) environmental model
Keyword(4) Markov game
Keyword(5) pursuit problem
1st Author's Name Yasuo Nagayuki
1st Author's Affiliation Nara Institute of Science and Technology()
2nd Author's Name Minoru Ito
2nd Author's Affiliation Nara Institute of Science and Technology
Date 2001/7/16
Paper # OFS2001-10,AI2001-15
Volume (vol) vol.101
Number (no) 210
Page pp.pp.-
#Pages 8
Date of Issue