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  • Automatic learning of hydrogen-bond fixes in an AMBER RNA force field

    Authors: Thorben Fröhlking, Vojtěch Mlýnský, Michal Janeček, Petra Kührová, Miroslav Krepl, Pavel Banáš, Jiří Šponer, Giovanni Bussi
    Submitted by giovannibussi      

    Why should we attempt to reproduce this paper?

    We do care about reproducibility. In case we receive any feedback, we would be really happy to improve our Github repository and/or submitted manuscript so as to make the reproduction easier!

  • Molecular Dynamics of Solids at Constant Pressure and Stress Using Anisotropic Stochastic Cell Rescaling

    Authors: Vittorio Del Tatto, Paolo Raiteri, Mattia Bernetti, Giovanni Bussi
    DOI: 10.3390/app12031139
    Submitted by giovannibussi      

    Why should we attempt to reproduce this paper?

    We do care about reproducibility. In case we receive any feedback, we would be really happy to improve our Github repository so as to make the reproduction easier!

  • PlanGAN: Model-based Planning With Sparse Rewards and Multiple Goals

    Authors: Henry Charlesworth and Giovanni Montana
    Submitted by gmontana74      
      Mean reproducibility score:   10.0/10   |   Number of reviews:   1
    Why should we attempt to reproduce this paper?

    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?