A Survey of Reinforcement Learning Based Routing Protocols for Mobile Ad-Hoc Networks

Designing mobility and power aware routing protocols have made the main focus of the early contributions to the field of Mobile Ad-hoc NETworks (MANETs). However, almost all conventional routing protocols for MANETs suffer from their lack of adaptivity leading to their performance degradation under varying network conditions. In fact, this is due to both simplistic conception hypotheses they made about the network and to the use of some prefixed parameters in protocols implementations. Currently, artificial intelligence methods like Reinforcement Learning (RL) are widely used to design adaptive routing strategies for MANETs. In this paper, we present a comprehensive survey of RL-based routing protocols for MANETs. Besides, we propose some future research directions in this area.