On Sample-Efficient Offline Reinforcement Learning: Data Diversity, Posterior Sampling, and Beyond

We seek to understand what facilitates sample-efficient learning from historical datasets for sequential decision-making, a problem that is popularly known as offline reinforcement learning (RL). Further, we are interested in algorithms that enjoy sample efficiency while leveraging (value) function approximation. In this paper, we address these fundamental questions by (i) proposing a notion of data diversity that subsumes the previous notions of coverage measures in offline RL and (ii) using this notion to {unify} three distinct classes of offline RL …

algorithms approximation beyond cs.ai cs.lg data data diversity datasets decision diversity efficiency function making offline paper posterior questions reinforcement reinforcement learning sample sampling stat.ml value