Presentation | 2009-07-13 Composition of Feature Space and State Space Dynamics Models for Model-based Reinforcement Learning Akihiko YAMAGUCHI, Jun TAKAMATSU, Tsukasa OGASAWARA, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Learning a dynamics model and a reward model during reinforcement learning is a useful way, since the agent can also update its value function by using the models. In this paper, we propose a general dynamics model that is a composition of the feature space dynamics model and the state space dynamics model. This way enables to obtain a good generalization from a small number of samples because of the linearity of the state space dynamics, while it does not lose the accuracy. We demonstrate the simulation comparison of some dynamics models used together with a Dyna algorithm. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Model-based reinforcement learning / Dyna-style planning / prioritized sweeping / dynamics model |
Paper # | NLP2009-15,NC2009-8 |
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Committee | NLP |
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Conference Date | 2009/7/6(1days) |
Place (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Nonlinear Problems (NLP) |
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Language | ENG |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Composition of Feature Space and State Space Dynamics Models for Model-based Reinforcement Learning |
Sub Title (in English) | |
Keyword(1) | Model-based reinforcement learning |
Keyword(2) | Dyna-style planning |
Keyword(3) | prioritized sweeping |
Keyword(4) | dynamics model |
1st Author's Name | Akihiko YAMAGUCHI |
1st Author's Affiliation | Graduate School of Information Science, Nara Institute of Science and Technology() |
2nd Author's Name | Jun TAKAMATSU |
2nd Author's Affiliation | Graduate School of Information Science, Nara Institute of Science and Technology |
3rd Author's Name | Tsukasa OGASAWARA |
3rd Author's Affiliation | Graduate School of Information Science, Nara Institute of Science and Technology |
Date | 2009-07-13 |
Paper # | NLP2009-15,NC2009-8 |
Volume (vol) | vol.109 |
Number (no) | 124 |
Page | pp.pp.- |
#Pages | 6 |
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