Application of Reinforcement Learning in Development of a New Adaptive Intelligent Traffic Shaper

In this paper, we have taken advantage of reinforcement learning to develop a new traffic shaper in order to obtain a reasonable utilization of bandwidth while preventing traffic overload in other part of the network and as a result, reducing total number of packet dropping in the whole network.. We used a modified version of Q-learning in which a combination of neural networks keeps the data of Q-table in order to make the operation faster while keeping the required storage as small as possible. This method shows satisfactory results in simulations from the aspects of keeping dropping probability low while injecting as many packets as possible into the network in order to utilize the free bandwidth as much as possible. On the other hand the results show that the system can perform in situations that are not originally designed to act in