Intrinsically-motivated reinforcement learning for control with continuous actions

We propose a more practical method to use empowerment as intrinsic reward within a reinforcement learning setting when states and actions are continuous. Our method builds upon two ideas: i) To take advantage of a new Bellman-like equation of empowerment and ii) to simplify the computation of the local rewards by avoiding the approximation of complex distributions over continuous states and actions.