DayDreamer learns compact discrete representations of the environment using recurrent neural networks that predict sequences given actions, reconstructing inputs and predicting rewards from recurrent states. The system trains robots using on-policy reinforcement learning purely inside the learned world model representation space, enabling farsighted behavior without simulators.