Reinforcement Learning Based Routing Protocols Analysis for Mobile Ad-Hoc Networks

Abstract

Energy consumption and maximize lifetime routing in Mobile Ad hoc Network (MANETs) is one of the most important issues. In our paper, we compare a global routing approach with a local routing approach both using reinforcement learning to maximize lifetime routing. We first propose a global routing algorithm based on reinforcement learning algorithm called Q-learning then we compare his results with a local routing algorithm called AODV-SARSA. Average delivery ratio, End to end delay and Time to Half Energy Depletion are used like metrics to compare both approach.