Explainable Reinforcement Learning Through a Causal Lens

Understand

Prevalent theories in cognitive science propose that humans understand and represent the knowledge of the world through causal relationships.

• In making sense of the world, we build causal models in our mind to encode cause-effect relations of events and use these to explain why new events happen.
• In this paper, we use causal models to derive causal explanations of behaviour of reinforcement learning agents.
• We present an approach that learns a structural causal model during reinforcement learning and encodes causal relationships between variables of interest.