| Presentation | 2004/10/12 Reinforcement Learning with State Segmentation Method based on Sensory Change Anticipations Hisashi HANDA, |
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| PDF Download Page | PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | The situatedness is one of the most important notion for constructing state segmentation of the reinforcement learning agents. Hence, we propose a new state segmentation method referring to sensation-action series. The proposed method quantizes input space, and anticipates the next inputs as a consequence of actions of agents. Moreover, the proposed method constitutes the state segmentation of agents based on anticipation accuracy. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | reinforcement learning / growing neural gas / incremental state segmentation method |
| Paper # | NC2004-81 |
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| Conference Information | |
| Committee | NC |
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| Conference Date | 2004/10/12(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
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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) | Reinforcement Learning with State Segmentation Method based on Sensory Change Anticipations |
| Sub Title (in English) | |
| Keyword(1) | reinforcement learning |
| Keyword(2) | growing neural gas |
| Keyword(3) | incremental state segmentation method |
| 1st Author's Name | Hisashi HANDA |
| 1st Author's Affiliation | Faculty of Engineering, Okayama University() |
| Date | 2004/10/12 |
| Paper # | NC2004-81 |
| Volume (vol) | vol.104 |
| Number (no) | 349 |
| Page | pp.pp.- |
| #Pages | 5 |
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