NeurIPS 2019
Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center
Paper ID:3340
Title:Fully Parameterized Quantile Function for Distributional Reinforcement Learning

The reviewers expressed some concerns about the significance of the paper, given that the main contribution is a SOTA result. However, they conclude that the Atari benchmark is sufficiently mature that an increase in this direction is of general interest. Some of the sticking points that should be addressed in the revision are: 1) consider performing additional empirical analysis to better understand how the method operates, 2) include further details (as requested by the reviewers).