Presentation 2004/3/9
A Fast Learning Method for Reinforcement Learning on Shared Memory Multiprocessors
Kouichirou MORI, Hayato YAMANA,
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Abstract(in English) In Reinforcement Learning, the agent learns by trial and error from a state without knowledge. Therefore, reinforcement learning has drawbacks that learning is slow. It is a serious problem how learns at high speed. In order to learn at high speed, some methods have been proposed. In the methods, the value function is divided. Then each divided value function is assigned to each processor, and updated in parallel. However, the method needs to exchange experiences frequently between the divided value function because of the character of reinforcement learning. It was a problem in the previous research that the overhead of communication between processors is large. In this paper, we propose the small overhead method that the parallel agents evaluate the shared value function asynchronously. As a result of the evaluation using shared memory type parallel machine, IBM pSeries 690, our method is approximately 22.2 times faster than sequential one in maze problem.
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Keyword(in English) Reinforcement Learning / Speed-up Learning / Parallel Processing
Paper # AI2003-91
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Conference Information
Committee AI
Conference Date 2004/3/9(1days)
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Registration To Artificial Intelligence and Knowledge-Based Processing (AI)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) A Fast Learning Method for Reinforcement Learning on Shared Memory Multiprocessors
Sub Title (in English)
Keyword(1) Reinforcement Learning
Keyword(2) Speed-up Learning
Keyword(3) Parallel Processing
1st Author's Name Kouichirou MORI
1st Author's Affiliation Graduate School of Science and Engineering, Waseda University()
2nd Author's Name Hayato YAMANA
2nd Author's Affiliation Faculty of Science and Engineering, Waseda University
Date 2004/3/9
Paper # AI2003-91
Volume (vol) vol.103
Number (no) 725
Page pp.pp.-
#Pages 6
Date of Issue