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D. B. Gu, and E. F. Yang, “A policy gradient reinforcement learning algorithm with fuzzy function approximation,” in Proceedings of the 2004 IEEE International Conference on Robotics and Biomimetics, Shenyang, China, pp. 934-940, August 2004.

has been cited by the following article:

• TITLE: A Genetic Based Fuzzy Q-Learning Flow Controller for High-Speed Networks

  AUTHORS: Xin LI, Yuanwei JING, Nan JIANG, Siying ZHANG

  KEYWORDS: High-Speed Network, Flow Control, Fuzzy Q-learning, Genetic Operator

  JOURNAL NAME: International Journal of Communications, Network and System Sciences, Vol.2 No.1, February 26, 2009

  ABSTRACT: For the congestion problems in high-speed networks, a genetic based fuzzy Q-learning flow controller is proposed. Because of the uncertainties and highly time-varying, it is not easy to accurately obtain the complete information for high-speed networks. In this case, the Q-learning, which is independent of mathematic model, and prior-knowledge, has good performance. The fuzzy inference is introduced in order to facilitate generalization in large state space, and the genetic operators are used to obtain the consequent parts of fuzzy rules. Simulation results show that the proposed controller can learn to take the best action to regulate source flow with the features of high throughput and low packet loss ratio, and can avoid the occurrence of congestion effectively.