| Presentation | 2000/5/18 Multi-agent reinforcement learning methods using the estimation of the other agent's internal model Nagayasu Yasuo, Ishii Shin, |
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| PDF Download Page | PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | The application of reinforcement learning to multi-agent systems has attracted recent attention. In a multi-agent environment, whether one agent's action is good or not depends on the other agents. We formerly proposed a multi-agent reinforcement learning method based on the estimation of the other agent's internal model. There, the internal model was estimated by the observation of past actions of the other agent. In this study, we consider a self-reference method for estimating the other agent's internal model. By using the pursuit problem as a testbed, we compare the two estimation methods. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | multi-agent reinforcement learning / Q-learning / internal model / pursuit problem / self-reference |
| Paper # | AI2000-4 |
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| Committee | AI |
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| Conference Date | 2000/5/18(1days) |
| Place (in Japanese) | (See Japanese page) |
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| Topics (in Japanese) | (See Japanese page) |
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| Paper Information | |
| 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 methods using the estimation of the other agent's internal model |
| Sub Title (in English) | |
| Keyword(1) | multi-agent reinforcement learning |
| Keyword(2) | Q-learning |
| Keyword(3) | internal model |
| Keyword(4) | pursuit problem |
| Keyword(5) | self-reference |
| 1st Author's Name | Nagayasu Yasuo |
| 1st Author's Affiliation | Nara Institute of Science and Technology() |
| 2nd Author's Name | Ishii Shin |
| 2nd Author's Affiliation | Nara Institute of Science and Technology |
| Date | 2000/5/18 |
| Paper # | AI2000-4 |
| Volume (vol) | vol.100 |
| Number (no) | 88 |
| Page | pp.pp.- |
| #Pages | 8 |
| Date of Issue | |