Presentation | 2003/7/22 Lempel-Ziv Coding for Measuring Complexity in Reinforcement Learning Kazunori IWATA, Kazushi IKEDA, Hideaki SAKAI, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | We describe Markov decision processes as the representation of Markov information sources to uniformly deal with reinforcement learning processes. We then consider an information theoretic analysis of the effects of the domain size and the complexity on the learning process and give a guide of strategy for recognizing the probabilistic structure of Markov decision processes as early as possible. In experimental results, we confirm that early stages of the learning process are mainly characterized by the domain size and as the number of steps increases it depends heavily on the stochastic complexity of Markov decision processes. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Reinforcement Learning / Markov Decision Process / Lempei-Ziv Coding / Domain Size / Stochastic Complexity |
Paper # | NC2003-43 |
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Committee | NC |
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Conference Date | 2003/7/22(1days) |
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Registration To | Neurocomputing (NC) |
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Language | ENG |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Lempel-Ziv Coding for Measuring Complexity in Reinforcement Learning |
Sub Title (in English) | |
Keyword(1) | Reinforcement Learning |
Keyword(2) | Markov Decision Process |
Keyword(3) | Lempei-Ziv Coding |
Keyword(4) | Domain Size |
Keyword(5) | Stochastic Complexity |
1st Author's Name | Kazunori IWATA |
1st Author's Affiliation | Department of System Science, Graduate School of Informatics, Kyoto University() |
2nd Author's Name | Kazushi IKEDA |
2nd Author's Affiliation | Department of System Science, Graduate School of Informatics, Kyoto University |
3rd Author's Name | Hideaki SAKAI |
3rd Author's Affiliation | Department of System Science, Graduate School of Informatics, Kyoto University |
Date | 2003/7/22 |
Paper # | NC2003-43 |
Volume (vol) | vol.103 |
Number (no) | 228 |
Page | pp.pp.- |
#Pages | 6 |
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