However, most methods rely heavily on music domain knowledge.

In this section, we provide further details for our data modeling. We note the difficulties of appropriately modeling the terminal variable which is a binary variable compared to the rest of the dimensions which are continuous for the environments we investigate. This is particularly challenging for "expert" datasets where early termination is rare. An immediate advantage of sampling data from a generative model is compression. As we discuss in Appendix B.3, sampling is fast ER provides high levels of dataset compression without sacrificing downstream performance in offline reinforcement learning.

Open-vocabulary object detection via vision and language knowledge distillation.

Our work applies DreamerV3's tricks to PPO and is the first such empirical study outside of the original work.