Presentation | 2010-03-10 Autonomous Composition of State Space Based on Action History Table in Reinforcement Learning Michio Kimura, Hidehiro Nakano, Arata Miyauchi, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | In the reinforcement learning, the discrete state space is generally constructed for red environments, and learning agents learn the policy in to each state. A high-dimensional and detailed discretization is often needed in the state input in the real environments. In order to learn practical tasks, it is necessary to reduce a huge computational complexity because of an increase in the number of states. Then, this study aims to reduce the computational complexity in large-scale environments. And, more efficient learning method based on an autonomous composition of state space is presented. Concretely, a method for designing state sets that considers state transitions is proposed. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Reinforcement learning / Autonomous composition of state space / Speeding up |
Paper # | NLP2009-185 |
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Committee | NLP |
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Conference Date | 2010/3/2(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Nonlinear Problems (NLP) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Autonomous Composition of State Space Based on Action History Table in Reinforcement Learning |
Sub Title (in English) | |
Keyword(1) | Reinforcement learning |
Keyword(2) | Autonomous composition of state space |
Keyword(3) | Speeding up |
1st Author's Name | Michio Kimura |
1st Author's Affiliation | Tokyo City University() |
2nd Author's Name | Hidehiro Nakano |
2nd Author's Affiliation | Tokyo City University |
3rd Author's Name | Arata Miyauchi |
3rd Author's Affiliation | Tokyo City University |
Date | 2010-03-10 |
Paper # | NLP2009-185 |
Volume (vol) | vol.109 |
Number (no) | 458 |
Page | pp.pp.- |
#Pages | 6 |
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