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For the congestion problems in high-speed networks, a Q-learning model-independent 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. In this paper, the flow with higher...
For the congestion problems in high-speed networks, a multi-agent flow controller (MFC) based on Q-learning algorithm conjunction with the theory of Nash equilibrium is proposed. Because of the uncertainties and highly time-varying, it is not easy to accurately obtain the complete information for high-speed networks, especially for the multi-bottleneck case. The Nash Q-learning algorithm, which is...
In this paper, we show that max-min fair congestion control methods with a stable symmetric Jacobian remain stable under arbitrary feedback delay and the stability condition of such methods does not involve any of the delays. To demonstrate the practicality of the obtained result, we change the original algorithm in Kelly's work to become robust under random feedback delay and fixed constants of the...
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