A reinforcement learning approach for and scheduling packets in dynamic networks

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. Effective network routing means selecting the optimal communication paths. It can be modeled as a multiagent RL problem. We propose an adaptive routing and scheduling algorithm based on reinforcement learning techniques.