JACIII Vol.17 No.5 pp. 721-730
doi: 10.20965/jaciii.2013.p0721


Acceleration of Reinforcement Learning with Incomplete Prior Information

Kento Terashima, Hirotaka Takano, and Junichi Murata

Department of Electrical and Electronic Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan

March 20, 2013
June 19, 2013
September 20, 2013
reinforcement learning, Q-learning, option, prior information, forgetting factor
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.
Cite this article as:
K. Terashima, H. Takano, and J. Murata, “Acceleration of Reinforcement Learning with Incomplete Prior Information,” J. Adv. Comput. Intell. Intell. Inform., Vol.17 No.5, pp. 721-730, 2013.
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