Deep Generative Models for Offline Policy Learning: Tutorial, Survey, and Perspectives on Future Directions

Abstract

Deep generative models (DGMs) have demonstrated great success across various domains, particularly in generating texts and images using models trained from offline data. Similarly, data-driven decision-making also necessitates learning a generator function from the offline data to serve as the policy. Applying DGMs in offline policy learning exhibits great potential, and numerous studies have explored in this direction. However, this field still lacks a comprehensive review and so developments of different branches are relatively independent. In this paper, we provide the first systematic review on the applications of DGMs for offline policy learning. We cover five mainstream DGMs, including Variational Auto-Encoders, Generative Adversarial Networks, Normalizing Flows, Transformers, and Diffusion Models, and their applications in both offline reinforcement learning (offline RL) and imitation learning (IL). Offline RL and IL are two main branches of offline policy learning and are widely-adopted techniques for sequential decision-making. Notably, for each type of DGM-based offline policy learning, we distill its fundamental scheme, categorize related works based on the usage of the DGM, and sort out the development process of algorithms in that field. In addition, we provide in-depth discussions on DGMs and offline policy learning as a summary, based on which we present our perspectives on future research directions. This work offers a hands-on reference for the research progress in DGMs for offline policy learning, and aims to inspire improved DGM-based offline RL or IL algorithms. For convenience, we maintain a paper list on https://github.com/LucasCJYSDL/DGMs-for-Offline-Policy-Learning.