Batch Offline Reinforcement Learning - Part 1

Batch Offline Reinforcement Learning - Part 1

Explore offline reinforcement learning in this 59-minute lecture by Emma Brunskill from Stanford University, presented at the Theory of Reinforcement Learning Boot Camp hosted by the Simons Institute. Delve into theoretical questions, notation, and key concepts such as generalization, covariate shift, and value error. Examine common tasks, assumptions, and evaluation criteria in batch reinforcement learning. Engage with discussion questions on important sampling, unbiased policy estimates, and models. Gain insights into doubly robust estimators and critically analyze the potential for wishful thinking in this approach to machine learning.