Automatic Generation of Macro-Actions using Genetic Algorithm for Reinforcement Learning

Takeshi Tateyama; Seiichi Kawata; Toshiki Oguchi

doi:10.11499/sicep.2002.0.62.0

SICE Annual Conference 2002

DOI https://doi.org/10.11499/sicep.2002.0.62.0

会議情報

主催: The Society of Instrument and Control Engineers

共催: IEEE/Industiral Electronic Society, IEEE/Robotics and Automation Society, IEEE/Control System Society

Automatic Generation of Macro-Actions using Genetic Algorithm for Reinforcement Learning

Takeshi Tateyama, Seiichi Kawata, Toshiki Oguchi

著者情報

キーワード: reinforcement learning, macro-action, Semi-Markov Decision Processes, classifier system

会議録・要旨集フリー

p. 62

詳細

抄録

The main problem of reinforcement learning is that learning converges slowly. As one of the solution, McGovern proposed “macro-action”. However, a human expert needs to design macro-actions which adapt to an environment. In this paper, we propose a new method that enables to generate the macro-actions which adapt to the enviroment automatically using genetic algorithm.

責任著者(Corresponding author)

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