DocumentCode :
3276331
Title :
Reinforcement Learning for Image Understanding
Author :
Fengtao Xiang ; Zhengzhi Wang ; Xingsheng Yuan
Author_Institution :
Coll. of Electromech. Eng. & Autom., Nat. Univ. of Defense Technol. Changsha, Changsha, China
fYear :
2013
fDate :
16-18 Jan. 2013
Firstpage :
1102
Lastpage :
1105
Abstract :
Reinforcement Learning is one of the hottest issues in current AI research fields. It´s a effective method in solving some machine learning problems. It´s high efficiency, simpler programming, easier understanding, and better performance. Here I will share my understanding. If there are something wrong, thanks for correct. In reinforcement learning, the learner is a decision-making agent that takes actions in an environment and receives reward (or penalty) for its actions in trying to solve a problem. After a set of trial-and-error runs, it should learn the best policy, which is the sequence of actions that maximize the total reward.
Keywords :
image processing; learning (artificial intelligence); decision-making agent; image understanding; machine learning; reinforcement learning; Hidden Markov models; Image segmentation; Learning; Learning systems; Machine learning; Markov processes; Robots; Artificial Intelligence; Image Understanding; Machine Learning; Reinforcement Learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent System Design and Engineering Applications (ISDEA), 2013 Third International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4673-4893-5
Type :
conf
DOI :
10.1109/ISDEA.2012.261
Filename :
6456071
Link To Document :
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