An Improvised Location Aided Routing Protocol for Vehicular Ad-hoc Network using Reinforced Learning

Vehicular Ad-hoc Networks (VANETs) are dynamic networks formed among the vehicles travelling on road. They are prone to change in network topologies very quickly due to limited range of randomly moving vehicles. A vehicle is in range at a time and out of range the other moment. This limitation paved the way for development of routing protocols for them which are different from the routing protocols for mobile ad hoc networks. Since most of the vehicles quite often travel within a city, the routing protocol VANETs can also be made intelligent with the use of deep reinforcement learning (RL). With the help of deep reinforcement learning, the routing protocol will keep on learning with their experience and take suitable routing decision in the future.