Presentation | 2005/3/22 Reinforcement Learning under Dyanamic Emvironment : Sequential Monte Carlo with Sample Re-initialization Akio TANAKA, Yohei NAKADA, Takashi MATSUMOTO, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Reinforcement Learinng (RL) consists of an agent and its environment. The agent learns optimal or sub-optimal action sequence from the available reward. If an agent presumes static environment, while the actual environment changes, it has natural limitations in its performance. This study proposes a new RL algorithm which can cope with abrupt environmental changes within online Bayesian framework. Implementation is accomplished via "Sample Re-initialization" ("Sample Re-drawing") with Sequential Monte Carlo (SMC). |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Reinforcement Learning / Sequential Monte Carlo / Sample Re-initialization / Sample Re-drawing |
Paper # | NC2004-186 |
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Committee | NC |
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Conference Date | 2005/3/22(1days) |
Place (in Japanese) | (See Japanese page) |
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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 under Dyanamic Emvironment : Sequential Monte Carlo with Sample Re-initialization |
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Keyword(1) | Reinforcement Learning |
Keyword(2) | Sequential Monte Carlo |
Keyword(3) | Sample Re-initialization |
Keyword(4) | Sample Re-drawing |
1st Author's Name | Akio TANAKA |
1st Author's Affiliation | Graduate School of Science and Engineering, Waseda University:Core Research for Evolutional Science and Technology() |
2nd Author's Name | Yohei NAKADA |
2nd Author's Affiliation | Graduate School of Science and Engineering, Waseda University |
3rd Author's Name | Takashi MATSUMOTO |
3rd Author's Affiliation | Graduate School of Science and Engineering, Waseda University:Core Research for Evolutional Science and Technology |
Date | 2005/3/22 |
Paper # | NC2004-186 |
Volume (vol) | vol.104 |
Number (no) | 759 |
Page | pp.pp.- |
#Pages | 6 |
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