Technical Note: \cal Q-Learning
Machine Learning
A QoS-Provisioning neural fuzzy connection admission controller for multimedia high-speed networks
IEEE/ACM Transactions on Networking (TON)
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Nash q-learning for general-sum stochastic games
The Journal of Machine Learning Research
A Unified Analysis of Value-Function-Based Reinforcement Learning Algorithms
Neural Computation
Adaptive congestion protocol: A congestion control protocol with learning capability
Computer Networks: The International Journal of Computer and Telecommunications Networking
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Cooperative multiagent congestion control for high-speed networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hierarchical neuro-fuzzy call admission controller for ATM networks
Computer Communications
Value-function reinforcement learning in Markov games
Cognitive Systems Research
Congestion control mechanisms and the best effort service model
IEEE Network: The Magazine of Global Internetworking
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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 independent of mathematic model, shows the particular superiority in high-speed networks. It obtains the Nash Q-values through trial-and-error and interaction with the network environment to improve its behavior policy. By means of learning procedures, MFCs can learn to take the best actions to regulate source flow with the features of high throughput and low packet loss ratio. Simulation results show that the proposed method can promote the performance of the networks and avoid the occurrence of congestion effectively.