A Novel Routing Algorithm for Mobile ad-hoc Networks Based on Q-learning and its Generalization to FSR Routing Protocol

Abstract

This study proposes a novel routing algorithm using Q-learning. Q-learning is a machine learning (artificial intelligence) algorithm using the reinforcement learning policy which can be used to solve problems for which there are different ways to reach their goal. The proposed algorithm, the Modified Q-learning routing algorithm (MQRA), has eliminated the episodes of Q-learning required to gradually learn in different stages and this has made it a rapid routing algorithm. MQRA can be used in various types of networks. This study uses MQRA in mobile ad-hoc networks, its generalization to fisheye state routing (FSR) (a routing algorithm) and its performance results are compared with the standard FSR. Experimental results confirm the applicability and potential of the proposed algorithm.

Keywords

• routing algorithm
• mobile ad-hoc networks
• fsr protocol
• reinforcement learning
• routing in manets