Post Reinforcement Learning Inference

In “Post Reinforcement Learning Inference,” Vasilis Syrgkanis and Ruohan Zhan develop a new inferential framework for data collected via reinforcement learning (RL) algorithms, the adaptive systems that update strategies as outcomes unfold. Traditional statistical methods fail in this setting because adaptivity induces time-varying variance and dependence across samples. Syrgkanis and Zhan propose an adaptively weighted generalized method of moments (AW-GMM) estimator that stabilizes this variance through data-dependent weights. They prove that the weighted estimator achieves consistency and asymptotic normality, enabling valid hypothesis testing and confidence intervals for policy values and dynamic treatment effects. Their method provides a unified approach for structural estimation and inference under nonstationary, adaptively generated sequence data, with applications to dynamic off-policy evaluation and personalized decision systems.