| Presentation | 2006-06-16 Feature Extraction for Decision-Theoretic Planning in Partially Observable Stochastic Domains Hajime FUJITA, Yutaka NAKAMURA, Shin ISHII, |
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| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | We propose a feature extraction technique for decision-theoretic planning problems in partially observable stochastic domains and show a novel approach for solving them. To maximize an expected future reward in unknown environment, all the agent has to do is to estimate a Markov chain over a statistic variable related to rewards. In our approach, an auxiliary state variable whose stochastic process satisfies the Markov property, called internal state, is introduced to the model with the assumption that the rewards are dependent on a pair of an internal state and an action. The agent then estimates the dynamics of an internal state model based on the maximum likelihood inference along with acquiring its policy; the internal state model represents an essential feature necessary to decision-making. Computer simulation results show that our technique can find an appropriate feature for acquiring a good policy and achieve faster learning with fewer policy parameters than a conventional algorithm, in a reasonably sized partially observable problem. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Partially observable environments / Decision-theoretic planning / Internal state / Maximum likelihood inference / Feature extraction |
| Paper # | NC2006-24 |
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| Committee | NC |
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| Conference Date | 2006/6/9(1days) |
| Place (in Japanese) | (See Japanese page) |
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| 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) | Feature Extraction for Decision-Theoretic Planning in Partially Observable Stochastic Domains |
| Sub Title (in English) | |
| Keyword(1) | Partially observable environments |
| Keyword(2) | Decision-theoretic planning |
| Keyword(3) | Internal state |
| Keyword(4) | Maximum likelihood inference |
| Keyword(5) | Feature extraction |
| 1st Author's Name | Hajime FUJITA |
| 1st Author's Affiliation | Nara Institute of Science and Technology() |
| 2nd Author's Name | Yutaka NAKAMURA |
| 2nd Author's Affiliation | Nara Institute of Science and Technology |
| 3rd Author's Name | Shin ISHII |
| 3rd Author's Affiliation | Nara Institute of Science and Technology |
| Date | 2006-06-16 |
| Paper # | NC2006-24 |
| Volume (vol) | vol.106 |
| Number (no) | 102 |
| Page | pp.pp.- |
| #Pages | 6 |
| Date of Issue | |