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- W2396928060 abstract "Reinforcement learning is applicable to complex or unknown problems because the solution search process is done by trial-and-error. However, the calculation time for the trial-and-error search becomes larger as the scale of the problem increases. Therefore, in order to decrease calculation time, some methods have been proposed using the prior information on the problem. This paper improves a previously proposed method utilizing options as prior information. In order to increase the learning speed even with wrong options, methods for option correction by forgetting the policy and extending initiation sets are proposed." @default.
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- W2396928060 date "2013-09-20" @default.
- W2396928060 modified "2023-09-26" @default.
- W2396928060 title "Acceleration of Reinforcement Learning with Incomplete Prior Information" @default.
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- W2396928060 doi "https://doi.org/10.20965/jaciii.2013.p0721" @default.
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