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  • 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?

  • Optimizing the Use of Carbonate Standards to Minimize Uncertainties in Clumped Isotope Data

    Authors: Ilja J. Kocken, Inigo A. Müller, Martin Ziegler
    DOI: 10.1029/2019GC008545
    Submitted by japhir      

    Why should we attempt to reproduce this paper?

    Even though the approach in the paper focuses on a specific measurement (clumped isotopes) and how to optimize which and how many standards we use, I hope that the problem is general enough that insight can translate to any kind of measurement that relies on machine calibration. I've committed to writing a literate program (plain text interspersed with code chunks) to explain what is going on and to make the simulations one step at a time. I really hope that this is understandable to future collaborators and scientists in my field, but I have not had any code review internally and I also didn't receive any feedback on it from the reviewers. I would love to see if what in my mind represents "reproducible code" is actually reproducible, and to learn what I can improve for future projects!

  • Hyperparameter importance Across Datasets

    Authors: Jan N van Rijn and Frank Hutter
    DOI: 10.1145/3219819.3220058
    Submitted by hub-admin    
      Mean reproducibility score:   7.0/10   |   Number of reviews:   1
    Why should we attempt to reproduce this paper?

    I tried hard to make this paper as reproducible as possible, but as techniques and dependencies become more complex, it is hard to make it 100% clear. Any form of feedback is more than welcome.

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