In this paper, an R package was used to improve the reproducibility of the analyses. Therefore, it would be good to know to what extent this works. The R package includes the following analyses: (1) data trimming and preparation, (2) descriptive statistics, (3) reliability and correlations, (4) t-tests and Bayesian t-tests, (5) latent-change models (structural equation modeling approach), and (6) multiverse analyses. Furthermore, all deidentified data, experiment codes, research materials, and results are publicly accessible on the Open Science Framework (OSF) at https://osf.io/ngfxv. The study’s design and the analyses were pre-registered on OSF. The preregistration can be accessed at https://osf.io/ tywu7.
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?
In theory, reproducing this paper should only require a clone of a public Git repository, and the execution of a Makefile (detailed in the README of the paper repository at https://github.com/psychoinformatics-de/paper-remodnav). We've set up our paper to be dynamically generated, retrieving and installing the relevant data and software automatically, and we've even created a tutorial about it, so that others can reuse the same setup for their work. Nevertheless, we've for example never tried it out across different operating systems - who knows whether it works on Windows? We'd love to share the tips and tricks we found to work, and even more love feedback on how to improve this further.
