This paper presents a Price-Reward learning scheme to encourage mutual coordination between mobile nodes and their wireless networks. In order to maximize the overall network coverage through cooperative diversity, a Nash-Stackelberg Multiplicative Weighted Imitative CODIPAS-RL scheme is proposed based on our previous work. The wireless network implements a 2-level Stackelberg game by introducing Price-Reward (lambda,µ) parameters whereas the Reinforcement Learning (RL) scheme paves the way for mobile nodes to reach a Nash-Equilibrium state. The performance evaluation of the learning scheme for the presented scenario proves fast convergence towards the optimal solution by adopting different sets of actions for the selected strategies. This ensures QoS sustainability during handover situations by data relaying and avoids collisions among mobile nodes while accessing network resources.