Presentation 1999/1/12
MS-RL : Multi-Strategy Reinforcement Learning method for a learning agent under a variant environment
Mitsuyoshi OKAMOTO, Tomohiro YAMAGUCHI, Masahiko YACHIDA,
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Abstract(in English) The object of this research is to realize a robust and flexible learning agent under a variant environment with intermittent changes of the learning conditions. Reinforcement learning is one of the possible behavior learning methods for an agent that behaves robustly in an unknown environment. Most previous reinforcement learning researches assume the limited conditions such as MDP environment to guarantee a rationality for learning, and tend to seek the convergence of the optimal learning result in infinite learning time. This paper presents Multi-Strategy Parallel Reinforcement Learning method (MSP-RL, in short) that performs the several different reinforcement learning algorithms in parallel.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) the intermittent change of the environment / Reinforcement Learning / a stochastic gradient method / learning parameter
Paper # AI98-73
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Committee AI
Conference Date 1999/1/12(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) MS-RL : Multi-Strategy Reinforcement Learning method for a learning agent under a variant environment
Sub Title (in English)
Keyword(1) the intermittent change of the environment
Keyword(2) Reinforcement Learning
Keyword(3) a stochastic gradient method
Keyword(4) learning parameter
1st Author's Name Mitsuyoshi OKAMOTO
1st Author's Affiliation Graduate School of Engineering Science, Osaka University()
2nd Author's Name Tomohiro YAMAGUCHI
2nd Author's Affiliation Graduate School of Engineering Science, Osaka University
3rd Author's Name Masahiko YACHIDA
3rd Author's Affiliation Graduate School of Engineering Science, Osaka University
Date 1999/1/12
Paper # AI98-73
Volume (vol) vol.98
Number (no) 499
Page pp.pp.-
#Pages 8
Date of Issue