An instrumental network routing algorithm for wireless networks base on the reinforcement-learning algorithms and network traffic

Reinforcement - Learning methods are widely used in routing problems. These methods interact with the network changes, so are called Adaptive routing methods. Q - Learning algorithms has some quantities which are labeled Q, and are known as headers in routing methods which apply this algorithm. As a result, if we add forward exploration to backward One, their header will be increased. We tried in this paper to decrease the headers of the packets by presenting some changes in above mentioned algorithms which lead to the increased throughput. This parameter is evaluated based on different networks throughput and is compared with present methods.