Review for NeurIPS paper: Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs

Summary and Contributions: The paper proposes a method for utilizing ODEs to represent dynamics for continuous-time decision-making problems with the aim of They also target filling a perceived gap in the literature of Deep RL for continuous-time problems, where most publications are model-free and discretize time if it is continuous. They claim that their approach leads to lower dependence on vast amounts of training data, better performance and that the model-based approach is well-founded. I tend to agree, although this is not exactly my area. I also believe the importance of connecting ODEs and other explicit models is critical for extending RL methods to important problems in physics, chemistry, epidemiology and population modelling.