Asymptotic Theory for IV-Based Reinforcement Learning with Potential Endogeneity

arXiv:2103.04021 · stat.ML, cs.LG, econ.EM, math.OC · Submitted 2026-08-20 · Read on arXiv

Jin Li, Ye Luo, Zigan Wang, Xiaowei Zhang

stat.ML, cs.LG, econ.EM, math.OC

Submitted: 2026-08-20

Updated: 2026-08-21

Terminology

• Q-learning — 70× in this paper · explained in 3 episodes
• Actor — 33× in this paper · explained in 3 episodes
• Reinforcement Learning — 12× in this paper · explained in 4 episodes
• Reward Function — 9× in this paper · explained in 2 episodes
• Action Space — 5× in this paper · explained in 1 episode
• Contextual Bandit Problem — 2× in this paper · explained in 2 episodes
• Reinforcement Learning (RL) — 2× in this paper · explained in 24 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Contextual Bandits — 1× in this paper · explained in 3 episodes
• Convergence Guarantees — 1× in this paper · explained in 3 episodes
• Curse of Dimensionality — 1× in this paper · explained in 6 episodes
• Feature Engineering — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Hilbert Space — 1× in this paper · explained in 2 episodes
• IV-Actor Critic — 1× in this paper

Sources

• Dynamic Selection in Algorithmic Decision-making

Related papers

• Behavior of prediction performance metrics with rare events
• Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump ODEs
• A Posterior-Dynamics Framework for Imaging Inverse Problems with Pretrained Diffusion Priors
• One Permutation Is All You Need: Fast, Deterministic Feature Importance and Model Stress-Testing
• Online Conformal Prediction for Non-Exchangeable Panel Data
• Deep Time-Series Forecasting in 10 Years: A Survey