| 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) |
| Place (in Japanese) | (See Japanese page) |
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| Topics (in Japanese) | (See Japanese page) |
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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 |
| Sub Title (in English) | |
| 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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