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Actually ,various kinds of sources (such as voice, video or data) with diverse traffic characteristics and Quality of Service Requirements (QOS), which are multiplexed at very high rates, leads to significant traffic problems such as packet losses, transmission delays, delay variations, etc, caused mainly by congestion in the networks. The prediction of these problems in real time is quite difficult, making the effectiveness of "traditional" methodologies based on analytical models questionable. This article proposed and evaluates a QOS routing policy in packets topology and irregular traffic of communications network called Widest K-shortest paths Q-Routing. The technique used for the evaluation signals of reinforcement is Q-learning. Compared to standard Q-Routing, the exploration of paths is limited to K best non loop paths in term of hops number (number of routers in a path) leading to a substantial reduction of convergence time. In this work a proposal for routing which improves the delay factor and is based on the Reinforcement Learning is Concerned. We use Q-Learning as the Reinforcement Learning technique and introduce K-Shortest idea into the Learning Process. The proposed algorithm are applied to two different topologies. The OPNET is used to evaluate the performance of the proposed algorithm. The algorithm evaluation is done for two traffic Conditions, namely low load and high load.