Presentation | 1999/7/19 Acceleration of Reinforcement Learning Process Based out Action Allocated Multi Agents Shuji Nakamura, Masaki Sano, Yasiji Sawada, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | This paper describes an algorithm to speed up Q-Learning which is often used in reinforcement learning. Usually this learning method needs much time and computational resources, because it evaluates state-action pairs in high dimensional space. We propose the algorithm that positively regard single agent as multi agents and divide decision for outputs (actions). It reduces the number of the iterations until converge, calculation time, and memory size since search space is reduced. Besides it, we evaluate this algorithm and show some applications. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | reinforcement learning / acceleration / multi agent / hierarchical |
Paper # | NC99-34 |
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Committee | NC |
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Conference Date | 1999/7/19(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) | Acceleration of Reinforcement Learning Process Based out Action Allocated Multi Agents |
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Keyword(1) | reinforcement learning |
Keyword(2) | acceleration |
Keyword(3) | multi agent |
Keyword(4) | hierarchical |
1st Author's Name | Shuji Nakamura |
1st Author's Affiliation | Graduate School of Information Science, Tohoku Univ.() |
2nd Author's Name | Masaki Sano |
2nd Author's Affiliation | Research Institute of Electrical Communication, Tohoku Univ. |
3rd Author's Name | Yasiji Sawada |
3rd Author's Affiliation | Research Institute of Electrical Communication, Tohoku Univ. |
Date | 1999/7/19 |
Paper # | NC99-34 |
Volume (vol) | vol.99 |
Number (no) | 193 |
Page | pp.pp.- |
#Pages | 8 |
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