A Study on Abstract Policy for Acceleration of Reinforcement Learning

• 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/7452/1/A_Study_on_Abstract_Policy_for_Acceleration_of_Reinforcement_Learning.pdf; http://umpir.ump.edu.my/id/eprint/7452/; http://dx.doi.org/10.1109/SICE.2014.6935300

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...