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...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2014
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/7452/ 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 |