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Ad Hoc Networks are infrastructure less network in which nodes are connected by Multi-hop wireless links. Each node is acting as a router as it supports distributed routing. Routing challenges occurs as there are frequent path breaks due to the mobility. Various application domains include military applications, emergency search and rescue operations and collaborative computing. The existing protocols used are divided into proactive and on demand routing protocols. The various new routing algorithms are also designed to optimize the performance of a network in terms of various performance parameters. Dual reinforcement routing is learning based approach used for routing. This paper describes the implementation, mathematical evaluation and judging the performance of a network and analyze it to find the performance of a network.

This paper describes and evaluates the performance of various reinforcement learning algorithms with shortest path algorithms that are widely used for routing packets throughout the network. Shortest path routing is simplest policy used for routing the packets along the path having minimum number of hops. In high traffic or high mobility conditions, the shortest path gets flooded with huge number of packets and congestions occurs, so such shortest path does not provide the shortest path and increases delay for reaching the packets to the destination. Reinforcement learning algorithms are adaptive algorithms where the path is selected based on the traffic present on the network at real time. Thus they guarantee the least delivery time to reach the packets to the destination. Analysis is done on a 6-by-6 irregular grid and sample ad hoc network shows that performance parameters used for judging the network such as packet delivery ratio and delay provide optimum results using reinforcement learning algorithms.