A computer-implemented method of reinforcement learning for controlling steering of a vehicle, the method comprising: inputting training data relating to a subject system comprising the vehicle, 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 subject system with a succession of said actions and comprises experience data comprising a plurality of transitions, each transition comprising respective 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, wherein a state is defined by image data, sound data or sensory information from one or more sensors; and training a second neural network, the second neural network being an instance of the same neural network as a first neural network, wherein training the second neural network comprises: selecting a transition from the experience data; generating, with the first neural network, a target action-value parameter for the selected transition; updating weights of the second neural network based on the difference between the target generated by the first neural network and an action-value parameter generated by the second neural network; the method further comprising: during the training, updating weights of said first neural network based on the updated weights of said second neural network, wherein updating of said first neural network from said second neural network is performed at intervals; and wherein the second neural network is configured to provide an output to an action selector for use in selecting output data defining a selected action for controlling steering of the vehicle. inputting training data relating to a subject system comprising the vehicle, 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 subject system with a succession of said actions and comprises experience data comprising a plurality of transitions, each transition comprising respective 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, wherein a state is defined by image data, sound data or sensory information from one or more sensors; and training a second neural network, the second neural network being an instance of the same neural network as a first neural network, wherein training the second neural network comprises: selecting a transition from the experience data; generating, with the first neural network, a target action-value parameter for the selected transition; updating weights of the second neural network based on the difference between the target generated by the first neural network and an action-value parameter generated by the second neural network; selecting a transition from the experience data; generating, with the first neural network, a target action-value parameter for the selected transition; updating weights of the second neural network based on the difference between the target generated by the first neural network and an action-value parameter generated by the second neural network; the method further comprising: during the training, updating weights of said first neural network based on the updated weights of said second neural network, wherein updating of said first neural network from said second neural network is performed at intervals; and wherein the second neural network is configured to provide an output to an action selector for use in selecting output data defining a selected action for controlling steering of the vehicle. during the training, updating weights of said first neural network based on the updated weights of said second neural network, wherein updating of said first neural network from said second neural network is performed at intervals; and wherein the second neural network is configured to provide an output to an action selector for use in selecting output data defining a selected action for controlling steering of the vehicle.