Abstract: This tutorial will provide an introduction to the core ideas and results in offline RL theory, focusing on the setting of large state spaces and function approximation. Tentatively, the first part of the tutorial will establish the analyses of classic algorithms under the key assumptions on function approximation (such as Bellman-completeness) and the data distribution (i.e., coverage). The second part considers more advanced algorithms and analyses that rely on weaker or alternative assumptions, and extensions to novel settings beyond the standard single-agent MDPs. Participants are expected to be familiar with the theoretical foundation of MDPs (e.g., classic convergence analysis for tabular value iteration/policy iteration) and the basics of learning theory (e.g., concentration inequalities and union bound).