1. A method of reinforcement learning, the method comprising: obtaining training data relating to a subject system being interacted with by a reinforcement learning agent that performs actions from a set of actions to cause the subject system to move from one state to another state; wherein the training data comprises a plurality of transitions, each transition comprising respective starting state data, action data and next state data defining, respectively, a starting state of the subject system, an action performed by the reinforcement learning agent when the subject system was in the starting state, and a next state of the subject system resulting from the action being performed by the reinforcement learning system; and training a second neural network used to select actions to be performed by the reinforcement learning agent on the transitions in the training data and, for each transition, a respective target output generated by a first neural network, wherein the first neural network is another instance of the second neural network but with possibly different parameter values than those of the first neural network; and during the training, periodically updating the parameter values of the first neural network from current parameter values of the second neural network, wherein the state data and the next state data in each transition are image data.