Causal Reinforcement Learning: Unifying AI for Smarter Decision-Making.

This research proposes a novel paradigm called Causal Reinforcement Learning (CRL), which bridges the gap between causal inference and reinforcement learning. Traditionally, these two fields have developed independently, despite both dealing with counterfactual relationships – what would happen if circumstances were different. CRL formalizes the environment an AI agent operates in as a structural causal model, allowing for a unified treatment of various learning modes like online, off-policy, and causal calculus learning. The framework extends beyond existing modalities, introducing new dimensions for analysis such as generalized policy learning, where to intervene, imitation learning, and counterfactual learning. By explicitly incorporating causal principles, CRL aims to enhance an agent's ability to reason about interventions and predict outcomes more accurately, even in unseen scenarios. This integration promises to unlock new learning opportunities for AI systems.