In this paper, a new intelligent traffic shaper is proposed 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. This approach trains an intelligent agent to learn an appropriate value for token generation rate of a Token Bucket at various states of the network. 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 by minimization of used buffer size at each router in order to keep the delay occurred by packets waiting in long buffers to be sent, as small as possible.