DocumentCode
1632123
Title
An improved method of HRL based on BP neural network
Author
Hu, Kun ; Yu, Xue-Li
Author_Institution
Dept. of Comput. Sci. & Technol., Taiyuan Univ. of Technol., Taiyuan, China
Volume
1
fYear
2012
Firstpage
334
Lastpage
337
Abstract
An improved method of hierarchical reinforcement learning which named BMAXQ was presented in order to resolve the shortcomings of MAXQ. It amended the abstract mechanism of MAXQ and utilized the peculiarities of BP neural network. This method can make agent to find the subtasks automatically and realize parallel learning for every layer. It can be adapted to the learning task during the dynamic environment.
Keywords
learning (artificial intelligence); neural nets; BMAXQ; HRL; backpropagetiion neural network; hierarchical reinforcement learning; learning task; parallel learning; Abstracts; Algorithm design and analysis; Biological neural networks; Heuristic algorithms; Learning; Partitioning algorithms; BP Neural Network; Hierarchical Reinforcement Learning; MAXQ; Subtask;
fLanguage
English
Publisher
ieee
Conference_Titel
Instrumentation & Measurement, Sensor Network and Automation (IMSNA), 2012 International Symposium on
Conference_Location
Sanya
Print_ISBN
978-1-4673-2465-6
Type
conf
DOI
10.1109/MSNA.2012.6324581
Filename
6324581
Link To Document