We develop a method for learning index rules for multi-armed bandits, restless bandits, and dynamic resource allocation where the underlying transition probabilities and reward structure of the system is not known. Our approach builds on an understanding of both stochastic optimisation (specifically, the Whittle index) and reinforcement learning (specifically, Q-learning). We propose a novel implementation of Q-learning, which exploits the structure of the problem considered, in which the algorithm maintains two sets of Q-values for each project: one for reward and one for resource consumption. Based on these ideas we design a learning algorithm and illustrate its performance by comparing it to the state-of-the-art Q-learning algorithm for the Whittle index by Avrachenkov and Borkar. Both algorithms rely on Q-learning to estimate the Whittle index policy, however the nature in which Q-learning is used in each algorithm is dramatically different. Our approach seems to be able to deliver similar or better performance and is potentially applicable to a much broader and more general set of problems.