Presentation | 2007-06-15 Reinforcement Learning for Cooperative Actions in a Partially Observable Multi-Agent System Yuki TANIGUCHI, Takeshi MORI, Shin ISHII, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | In this article, we apply a policy gradient-based reinforcement learning to allowing multiple agents to perform cooperative actions in a partially, observable environment. We introduce an auxiliary state variable, an internal state, whose stochastic process is Markov, for extracting important features of multi-agent's dynamics. Computer simulations show that every agent can identify an appropriate internal state model and acquire a good policy; this approach is shown to be more effective than a traditional memory-based method. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Partially observable environments / Internal state / Policy gradient method / Multi-agent system / Cooperative action |
Paper # | NC2007-15 |
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Committee | NC |
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Conference Date | 2007/6/7(1days) |
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Registration To | Neurocomputing (NC) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Reinforcement Learning for Cooperative Actions in a Partially Observable Multi-Agent System |
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Keyword(1) | Partially observable environments |
Keyword(2) | Internal state |
Keyword(3) | Policy gradient method |
Keyword(4) | Multi-agent system |
Keyword(5) | Cooperative action |
1st Author's Name | Yuki TANIGUCHI |
1st Author's Affiliation | Nara Institute of Science and Technology() |
2nd Author's Name | Takeshi MORI |
2nd Author's Affiliation | Nara Institute of Science and Technology |
3rd Author's Name | Shin ISHII |
3rd Author's Affiliation | Nara Institute of Science and Technology |
Date | 2007-06-15 |
Paper # | NC2007-15 |
Volume (vol) | vol.107 |
Number (no) | 92 |
Page | pp.pp.- |
#Pages | 5 |
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