| Presentation | 2006-01-24 Reinforcement Learning in High-dimensional Continuous State Spaces : A State Space Compression Method Based on Multivariate Analysis Hideki SATOH, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | A state space compression method based on multivariate analysis was developed and applied to reinforcement learning for high-dimensional continuous state spaces. First, useful components in the state variables of the environment are extracted and meaningless ones are removed by using multiple regression analysis. Next, the state space of the environment is compressed by using principal component analysis so that only a few principal components can express the dynamics of the environment. Then, a basis of a feature space for function approximation of a nonlinear environment is constructed based on orthonormal bases of the important principal components. A feature space is thus autonomously construct without preliminary knowledge of the environment, and the environment is effectively expressed in the feature space. An example synchronization problem for multiple logistic maps was solved using this method, demonstrating that it solves the curse of dimensionality and exhibits high performance without suffering from disturbance states. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | multivariate analysis / reinforcement learning / actor-critic / function approximation / high-dimensional state space |
| Paper # | NLP2005-99 |
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| Conference Information | |
| Committee | NLP |
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| Conference Date | 2006/1/17(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (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) | Reinforcement Learning in High-dimensional Continuous State Spaces : A State Space Compression Method Based on Multivariate Analysis |
| Sub Title (in English) | |
| Keyword(1) | multivariate analysis |
| Keyword(2) | reinforcement learning |
| Keyword(3) | actor-critic |
| Keyword(4) | function approximation |
| Keyword(5) | high-dimensional state space |
| 1st Author's Name | Hideki SATOH |
| 1st Author's Affiliation | School of Systems Information Science, Future University-Hakodate() |
| Date | 2006-01-24 |
| Paper # | NLP2005-99 |
| Volume (vol) | vol.105 |
| Number (no) | 547 |
| Page | pp.pp.- |
| #Pages | 6 |
| Date of Issue | |