1. A method of reinforcement learning, the method comprising: inputting training data relating to a subject system, the subject system having a plurality of states and, for each state, a set of actions to move from one of said states to a next said state; wherein said training data is generated by operating on said system with a succession of said actions and comprises starting state data, action data and next state data defining, respectively for a plurality of said actions, a starting state, an action, and a next said state resulting from the action; and training a second neural network using said training data and target values for said second neural network derived from a first neural network; the method further comprising: generating or updating said first neural network from said second neural network.