Explore a comprehensive lecture on batch and counterfactual reinforcement learning presented by Emma Brunskill from Stanford University at the 2019 ADSI Summer Workshop on Algorithmic Foundations of Learning and Control. Delve into advanced techniques for minimizing and understanding data requirements in decision-making processes, addressing challenges like covariate shift, and examining batch policy optimization with generalization bounds. Investigate the legacy of reinforcement learning in benefiting people, analyze importance sampling for RL batch policy evaluation, and discover the first proof of convergence to local optima for batch policy gradient. Examine experimental settings using HIV treatment simulators, explore strong generalization guarantees on policy performance, and study linear thresholding policies. Gain insights into advantage decomposition and the Advantage Doubly Robust (ADR) Estimator while pursuing the quest for batch policy optimization with generalization guarantees. Read more