IRLR: an Improved Reinforcement Learning-Based Routing Algorithm for Wireless Mesh Networks

Reinforcement learning-based routing (RLR) in wireless mesh networks has recently attracted the attention of several research groups. Several recent studies have demonstrated that RLR provides higher network -greedy policy toperforms better than traditional routing protocols. In most RLR protocols, nodes use an select data transmission routes and update their Q-value tables. With this policy, the best route is chosen with a high probability, corresponding to the exploitation phase. The remaining routes are chosen with low -greedy policy in RLR protocolsprobability, corresponding to the exploration phase. A challenge with the is that data packets transmitted in the exploration phase have a high dropped probability or a large end-toend delay because they traverse long routes. In this paper, we propose an improved RLR for wireless mesh -greedy policy in RLR bynetworks to further improve its performance. Our approach is to improve the generating additional control packets for transmission in the exploration phase. All data packets are transmitted during the exploitation phase. Simulation results using OMNeT++ showed that he posed algorithm increases packet delivery ratio by an average value from 0.2 to 0.6%, and uces latency with an average value from 0.20 to 0.23 ms compared to the basic reinforcement learning-based routing algorithm.