A method, device and system of prediction of a state of an object in the environment using a pre-trained action model defined by an action model neural network. A control system for an object comprises a plurality of sensors for sensing a current state and an environment in which the object is located, and a first neural network. Predicted subsequent states of the object in the environment are obtained using the action model and a current state of the object in the environment The action model maps a plurality of state-action pairs (s, a), each state-action pair encoding a state (s) of the object in the environment and an action (a) performed by the object to a predicted subsequent state (s′) of the object in the environment. An action that maximizes a value of a target, based at least on a reward for each of the predicted subsequent states, is determined. The determined action is caused to be performed.