Journal of Data Science

Reinforcement Learning (RL) is a powerful framework for sequential decision-making, enabling agents to optimize actions through interaction with their environment. While widely studied in computer science, statisticians have advanced RL by addressing challenges like uncertainty quantification, sample efficiency, and interpretability. These contributions are particularly impactful in healthcare, where RL complements Dynamic Treatment Regimes (DTRs), optimizing personalized medicine by tailoring treatments to individuals based on evolving characteristics. This paper serves as both a tutorial for statisticians new to RL and a review of its integration with statistical methodologies. It introduces foundational RL concepts, classical algorithms, and Q-learning variants, and highlights how statistical perspectives, especially causal inference, address challenges in DTRs. By bridging RL and statistical perspectives, the paper highlights opportunities to enhance decision-making in high-stakes domains like healthcare.