Advancing Offline Reinforcement Learning: Essential Theories and Techniques for Algorithm Developers

This tutorial will equip empirical reinforcement learning (RL) researchers, including graduate students, early-career researchers and industry practitioners, with a deep theoretical understanding of offline RL. By explaining the necessary and sufficient conditions for theoretical guarantees, participants will gain insights into the challenges of offline RL compared to supervised learning and online RL, including reliance on bootstrapping targets, partial state-action space coverage, and spurious data.

Participants will first explore essential conditions for theoretical guarantees under these challenges and their connection to empirical limitations, such as dataset quality and neural network expressivity. The session will also cover advanced techniques for overcoming the difficulties of Offline RL under more realistic, weaker theoretical assumptions, including pessimism and density ratio estimation. Additionally, Hybrid Reinforcement Learning (Hybrid RL) approaches that integrate offline data with online interactions will be introduced to enhance exploration and data efficiency. This tutorial equips algorithm developers and early-career researchers with the tools to improve offline RL applications by combining theoretical insights with practical algorithmic strategies.

Participants attending this tutorial need to know basic reinforcement learning principles, such as Markov Decision Processes, value functions, and the optimal Bellman operator. Little mathematical knowledge is required since the tutorial will not cover detailed math proofs. Prior knowledge of offline RL algorithms will be beneficial but optional.