Explore a comprehensive lecture on offline reinforcement learning, focusing on generalization and robustness. Delve into the challenges and potential applications of this learning paradigm, which uses pre-collected static datasets without further environment interaction. Examine a general model-based offline RL algorithm that demonstrates generalization in large-scale Markov Decision Processes and robustness in policy discovery. Investigate offline Imitation Learning, including an algorithm with polynomial sample complexity and state-of-the-art performance in continuous control robotics benchmarks. Cover topics such as empirical RL for large-scale problems, finite horizon MDPs, offline data collection and coverage, learning goals in offline RL, and traditional versus offline imitation learning.