Design of ABR flow controller based on reinforcement learning-PID method

For the congestion problems in asynchronous transfer mode (ATM) networks, the reinforcement learning method is used in the updating of the control parameters, and a controller based on reinforcement learning-PID method is proposed. This algorithm is independent of the mathematic model and priori-knowledge of networks. It obtains the knowledge through trial-and-error and interaction with the environment to improve its behavior policy. So it has the ability of self-learning. The controller forces the queue length at the bottleneck node to the desired value by adjusting the source traffic rate of the available bit rate (ABR) service and guarantees the stability of the system to avoid the occur of congestion. Simulation results show the effectiveness of the method.