An Introduction to Causal Reinforcement Learning

The article introduces the concept of Causal Reinforcement Learning (CRL), which integrates causal inference principles with reinforcement learning (RL) methodologies. It proposes a formalization of environments as structural causal models, allowing for a unified approach to various learning modalities, including online, off-policy, and imitation learning. This integration is significant for practitioners as it opens new avenues for optimizing RL policies by leveraging counterfactual reasoning, enhancing the understanding of agent behavior in complex environments.