Building perfectly curious machines: using structural causal modeling to define the ideal feature space at the learning baseline of curiosity-driven agents

The thesis develops an ideal inverse-dynamics learning algorithm which can learn the properties of the sensors and actuators under its control. The algorithm converges on an ideal feature space, where the implementation details of the actuators under the algorithm's control are rendered invisib...