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Automatic generation of macro-actions using genetic algorithm for reinforcement learning

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3 Author(s)
Tateyama, T. ; Graduate Sch. of Eng., Tokyo Metropolitan Univ., Japan ; Kawata, S. ; Oguchi, T.

The main problem of reinforcement learning is that the learning converges slowly. As one of the solutions, McGovern (1997) proposed the "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 one to generate the macro-actions which adapt to the environment automatically using the genetic algorithm.

Published in:

SICE 2002. Proceedings of the 41st SICE Annual Conference  (Volume:1 )

Date of Conference:

5-7 Aug. 2002

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