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

arXiv:2402.13777v1 Announce Type: new
Abstract: Deep generative models (DGMs) have demonstrated great success across various domains, particularly in generating texts, images, and videos using models trained from offline data. Similarly, data-driven decision-making and robotic control also necessitate learning a generator function from the offline data to serve as the strategy or policy. In this case, applying deep generative models in offline policy learning exhibits great potential, and numerous studies have explored in this direction. However, this field still lacks a …

abstract arxiv control cs.ai cs.lg data data-driven decision deep generative models dgms domains function future generative generative models generator images making offline perspectives policy robotic success survey tutorial type videos