A Study on Abstract Policy for Acceleration of Reinforcement Learning

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A Study on Abstract Policy for Acceleration of Reinforcement Learning

Reinforcement learning (RL) is well known as one of the methods that can be applied to unknown problems. However, because optimization at every state requires trial-and-error, the learning time becomes large when environment has many states. If there exist solutions to similar problems and they are...

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Bibliographic Details
Main Authors:	Ahmad Afif, Mohd Faudzi, Hirotaka, Takano, Junichi, Murata
Format:	Conference or Workshop Item
Language:	English
Published:	2014
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/7452/ http://umpir.ump.edu.my/id/eprint/7452/1/A_Study_on_Abstract_Policy_for_Acceleration_of_Reinforcement_Learning.pdf

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