[1]LIAN Chuanqiang,XU Xin,WU Jun,et al.Q-CF multiAgent reinforcement learningfor resource allocation problems[J].CAAI Transactions on Intelligent Systems,2011,6(2):95-100.
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Q-CF multiAgent reinforcement learningfor resource allocation problems

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 6 Number of periods: 2011 2 Page number: 95-100 Column: 学术论文—机器学习 Public date: 2011-04-25
Title:
Q-CF multiAgent reinforcement learningfor resource allocation problems
Author(s):
LIAN Chuanqiang;  XU Xin;  WU Jun;  LI Zhaobin
College of Mechatronics and Automation, National University of Defense Technology, Changsha 410073, China
Keywords:
multiAgent system;  reinforcement learning;  resource allocation;  cooperation control
CLC:
TP391.1
DOI:
-
Abstract:
When a multiAgent reinforcement learning algorithm is used in complex distributed systems, problems such as huge state space and low learning efficiency arise. In this paper, a multiAgent reinforcement learning algorithm was studied for the resource allocation problem in a network environment. By combining the Qlearning algorithm and the chain feedback learning mechanism, a novel QCF multiAgent reinforcement learning algorithm was presented. In the QCF algorithm, multiAgent cooperation was realized based on the mechanism of information chain feedback. Simulation results show that compared with the multiAgent Qlearning algorithm in existence, the proposed algorithm in this paper has a faster convergence speed while at the same time ensures the performance optimization of cooperation policy.
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