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 Japanese) | (See Japanese page) |
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. |
Keyword(in Japanese) | (See Japanese page) |
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 |
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Conference Date | 2001/7/16(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Artificial Intelligence and Knowledge-Based Processing (AI) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
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 |
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