Explore offline reinforcement learning in this 40-minute discussion moderated by Pablo Castro from Google. Delve into topics such as small vs large data sets, optimism under uncertainty, state coverage, and pragmatic vs conceptual approaches. Examine online R, policy evaluation, assumptions, and various types of uncertainty qualification and quantification. Investigate methods without constraints, offline data, guidelines for collecting data, and gain insights from expert answers to thought-provoking questions in the field of deep reinforcement learning.