  Empowerment quantifies the influence an agent has on its environment. This is
formally achieved by the maximum of the expected KL-divergence between the
distribution of the successor state conditioned on a specific action and a
distribution where the actions are marginalised out. This is a natural
candidate for an intrinsic reward signal in the context of reinforcement
learning: the agent will place itself in a situation where its action have
maximum stability and maximum influence on the future. The limiting factor so
far has been the computational complexity of the method: the only way of
calculation has so far been a brute force algorithm, reducing the applicability
of the method to environments with a small set discrete states. In this work,
we propose to use an efficient approximation for marginalising out the actions
in the case of continuous environments. This allows fast evaluation of
empowerment, paving the way towards challenging environments such as real world
robotics. The method is presented on a pendulum swing up problem.
