Packet Routing in Dynamic Network: Deep Reinforcement Learning-Based Approach

The mobility of nodes makes the topology of wireless ad hoc networks change frequently. The traditional routing protocols are usually based on fixed routing policies, which are difficult to adapt to the dynamic changes of the network. This paper investigates the packet routing optimization problem in wireless ad hoc networks by using deep reinforcement learning (DRL) technique. To seek the optimal transmission path between the source node and the destination node for a packet, average delivery time is applied to assess the transmission path while the routing Markov decision process (RMAP) model is utilized to formulate the routing optimization problem. Furthermore, a DQN-routing algorithm is developed to deal with such an optimization problem. The distinguished features of the proposed algorithm lie in the facts that it can reduce the average delivery time and the network congestion, is also suitable for dynamic topology network environment. Finally, a simulation example is given to verify the validity of the theoretical results.