Delves into Reinforcement Learning with Human Feedback, discussing convergence of estimators and introducing a pessimistic approach for improved performance.
Explores Monte-Carlo integration for approximating expectations and variances using random sampling and discusses error components in conditional choice models.
Covers model-free prediction methods in reinforcement learning, focusing on Monte Carlo and Temporal Differences for estimating value functions without transition dynamics knowledge.