Reinforcement learning congestion controller for multimedia traffic in high speed networks

The use of reinforcement learning scheme for congestion control in high-speed network is presented in this paper. Traditional methods perform congestion control by means of monitoring the queue length. When the queue length is greater than a predefined threshold, the source rate is decreased at a fixed rate. However, the determination of the congested threshold and sending rate is difficult for these methods. We adopted a simple reinforcement learning method, called Adaptive Heuristic Critic (AHC), to solve the problem. The AHC controller maintains an expectation of reward and takes the best policy to control source flow. By way of learning and then taking right actions, simulation results have shown that the approach can promote the system utilization and decrease packet loss.