| Presentation | 2005/3/22 Examination of Q-learning and detailed correction of the action policy of robot in real environment Yuu TAKAI, Hiroki SUYARI, |
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| PDF Download Page | PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | When a robot learns some action policies on real time for given environments, a large memory capacity and time are required. In order to reduce the required memory and time for given environments, we present a method of improving an action policy obtained in Q-learning. In other words, our method enables an action policy obtained in one environment to apply to other environment with small modification. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Reinforcement Learning / Q-learning |
| Paper # | NC2004-184 |
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| Committee | NC |
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| Conference Date | 2005/3/22(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
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| Paper Information | |
| 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) | Examination of Q-learning and detailed correction of the action policy of robot in real environment |
| Sub Title (in English) | |
| Keyword(1) | Reinforcement Learning |
| Keyword(2) | Q-learning |
| 1st Author's Name | Yuu TAKAI |
| 1st Author's Affiliation | Graduate School of Science and Technology, Chiba University() |
| 2nd Author's Name | Hiroki SUYARI |
| 2nd Author's Affiliation | Department of Information and Image Science, Chiba University |
| Date | 2005/3/22 |
| Paper # | NC2004-184 |
| Volume (vol) | vol.104 |
| Number (no) | 759 |
| Page | pp.pp.- |
| #Pages | 5 |
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