Empowerment Gain as Causal Learning, Causal Learning as Empowerment Gain: A Bridge between Bayesian Causal Hypothesis Testing and Reinforcement Learning

Abstract

Abstract Cognitive scientists have applied advances in our formal understanding of causation in philosophy and computer science, particularly within the Causal Bayes Net formalism, to understand human causal learning. In parallel, reinforcement learning (RL) researchers have developed the idea of an intrinsic reward signal called “empowerment.” An agent is rewarded for maximizing the mutual information between its actions and their outcomes, regardless of the external reward value of those outcomes. Empowerment may be an important bridge between classical Bayesian causal learning and RL and may help to characterize causal learning in humans and enable it in machines.