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 :
بازگشت