I tried as hard as possible to make it reproducible, which it is on my computer. I would be happy to see if this still works on other computers. Moreover, by allowing easy reproducibility, I hope that other people may easily build research on top of this work.
The method is trained on the data that were available, but it is meant to be re-trainable as soon as new data are published. It would be great to be really sure that even someone else will be able to do it. In case we receive any feedback, we would be really happy to improve our Github repository so as to make the reproduction easier!
Popular descriptors for machine learning potentials such as the Behler-Parinello atom centred symmetry functions (ACSF) or the Smooth Overlap of Interatomic Potentials (SOAP) are widely used but so far not much attention has been paid to optimising how many descriptor components need to be included to give good results.
This paper proposes a probabilistic planner that can solve goal-conditional tasks such as complex continuous control problems. The approach reaches state-of-the-art performance when compared to current deep reinforcement learning algorithms. However, the method relies on an ensemble of deep generative models and is computationally intensive. It would be interesting to reproduce the results presented in this paper on their robotic manipulation and navigation problems as these are very challenging problems that current reinforcement learning methods cannot easily solve (and when they do, they require a significantly larger number of experiences). Can the results be reproduced out-of-the-box with the provided code?
The current code is written in Torch, which is no longer actively maintained. Since deep learning in nanophotonics is an area of active interest (e.g. for the design of new metamaterials), it is important to update the code to use a more modern deep learning library such as tensorflow/keras