DocumentCode
1947792
Title
Improvement with Joint Rewards on Multi-agent Cooperative Reinforcement Learning
Author
Ying, Pan ; Dehua, Li
Author_Institution
Inst. for Pattern Recognition & Artificial Intell., Huangzhong Univ. of Sci. & Technol., Wuhan
Volume
1
fYear
2008
fDate
12-14 Dec. 2008
Firstpage
536
Lastpage
539
Abstract
Cooperation among agents is important for multi-agent system. In this paper, an improved cooperative reinforcement learning algorithm is proposed, which based on joint rewards to insure agents to learn cooperative behavior. Furthermore, a symmetry idea is included in the algorithm to reduce the states size of reinforcement learning. The experiment results show the efficiency and well convergence of the algorithm.
Keywords
learning (artificial intelligence); multi-agent systems; joint reward; multiagent cooperative reinforcement learning; Artificial intelligence; Computer science; Learning systems; Multiagent systems; Pattern recognition; Pursuit algorithms; Software engineering; State-space methods; Testing; Cooperative behavior; Joint reward; Reinforcement learning; Symmetry;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science and Software Engineering, 2008 International Conference on
Conference_Location
Wuhan, Hubei
Print_ISBN
978-0-7695-3336-0
Type
conf
DOI
10.1109/CSSE.2008.920
Filename
4721805
Link To Document