Empowerment: A Universal Agent-Centric Measure of Control 论文

2005引用 284
Evolutionary Algorithms and ApplicationsEvolutionary Game Theory and CooperationReinforcement Learning in Robotics

摘要

The classical approach to using utility functions suffers from the drawback of having to design and tweak the functions on a case by case basis. Inspired by examples from the animal kingdom, social sciences and games we propose empowerment, a rather universal function, defined as the information-theoretic capacity of an agent's actuation channel. The concept applies to any sensorimotor apparatus. Empowerment as a measure reflects the properties of the apparatus as long as they are observable due to the coupling of sensors and actuators via the environment. Using two simple experiments we also demonstrate how empowerment influences sensor-actuator evolution