Presentation | 2005-06-23 Improvement of State Generator Based on ART Neural Network for Reinforcement Learning Taisuke Nakamura, Takeshi Kamio, Kunihiko Mitsubori, Hisato Fujisaka, |
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Abstract(in English) | The trade-off between exploration and exploitation has often been discussed in studies on reinforcement learning (RL). This is because exploration and exploitation influence the quality of solutions and the learning efficiency respectively. Previously, we have proposed an adaptive state space segmentation method based on ART neural network for RL. This method is useful for not only the state space segmentation but also the balance between exploration and exploitation. However, if the exploration strength is too large, the learning efficiency degreases rapidly. Since the appropriate strength is generally unknown, this problem must be solved. In this report, we propose a new segmentation method based on ART with two learning phases to improve our conventional method in the tolerance of exploration strength. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Reinforcement Learning / ART Neural Network / State Space / Exploration / Exploitation |
Paper # | NLP2005-20 |
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Committee | NLP |
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Conference Date | 2005/6/16(1days) |
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Paper Information | |
Registration To | Nonlinear Problems (NLP) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Improvement of State Generator Based on ART Neural Network for Reinforcement Learning |
Sub Title (in English) | |
Keyword(1) | Reinforcement Learning |
Keyword(2) | ART Neural Network |
Keyword(3) | State Space |
Keyword(4) | Exploration |
Keyword(5) | Exploitation |
1st Author's Name | Taisuke Nakamura |
1st Author's Affiliation | Hiroshima City University() |
2nd Author's Name | Takeshi Kamio |
2nd Author's Affiliation | Hiroshima City University |
3rd Author's Name | Kunihiko Mitsubori |
3rd Author's Affiliation | Japan Coast Guard Academy |
4th Author's Name | Hisato Fujisaka |
4th Author's Affiliation | Hiroshima City University |
Date | 2005-06-23 |
Paper # | NLP2005-20 |
Volume (vol) | vol.105 |
Number (no) | 125 |
Page | pp.pp.- |
#Pages | 6 |
Date of Issue |