Efficient Empowerment

TL;DR

This paper introduces an efficient approximation method for calculating empowerment in continuous environments, enabling its application in complex, real-world scenarios like robotics.

Contribution

It proposes a novel, computationally efficient approximation for empowerment in continuous spaces, overcoming previous limitations of brute-force methods.

Findings

The method enables fast empowerment evaluation in continuous environments.

Application demonstrated on a pendulum swing-up task.

Facilitates future use in real-world robotics scenarios.

Abstract

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.