While deep reinforcement learning (DRL) has led to numerous successes in recent years, reproducing these successes can be extremely challenging. One reproducibility challenge particularly relevant to DRL is nondeterminism in the training process, which can substantially affect the results. Motivated by this challenge, we study the positive impacts of deterministic implementations in eliminating nondeterminism in training. To do so, we consider the particular case of the deep Q-learning algorithm, for which we produce a deterministic implementation by identifying and controlling all sources of nondeterminism in the training process. One by one, we then allow individual sources of nondeterminism to affect our otherwise deterministic implementation, and measure the impact of each source on the variance in performance. We find that individual sources of nondeterminism can substantially impact the performance of agent, illustrating the benefits of deterministic implementations. In addition, we also discuss the important role of deterministic implementations in achieving exact replicability of results.
Reproducibility in DRL has also been examined from the perspective of statistical hypothesis testing by Colas et. al @cite_1 . They describe good statistical practices for algorithm comparison as well the selection of an appropriate sample size in DRL.
@cite_1
2754517384 2512030163 2107295961 1574680346
In recent years, significant progress has been made in solving challenging problems across various domains using deep reinforcement learning (RL). Reproducing existing work and accurately judging the improvements offered by novel methods is vital to sustaining this progress. Unfortunately, reproducing results for state-of-the-art deep RL methods is seldom straightforward. In particular, non-determinism in standard benchmark environments, combined with variance intrinsic to the methods, can make reported results tough to interpret. Without significance metrics and tighter standardization of experimental reporting, it is difficult to determine whether improvements over the prior state-of-the-art are meaningful. In this paper, we investigate challenges posed by reproducibility, proper experimental techniques, and reporting procedures. We illustrate the variability in reported metrics and results when comparing against common baselines and suggest guidelines to make future results in deep RL more reproducible. We aim to spur discussion about how to ensure continued progress in the field by minimizing wasted effort stemming from results that are non-reproducible and easily misinterpreted. This report documents the program and the outcomes of Dagstuhl Seminar 16041 "Reproducibility of Data-Oriented Experiments in e-Science". In many subfields of computer science, experiments play an important role. Besides theoretic properties of algorithms or methods, their effectiveness and performance often can only be validated via experimentation. In most of these cases, the experimental results depend on the input data, settings for input parameters, and potentially on characteristics of the computational environment where the experiments were designed and run. Unfortunately, most computational experiments are specified only informally in papers, where experimental results are briefly described in figure captions; the code that produced the results is seldom available. This has serious implications. Scientific discoveries do not happen in isolation. Important advances are often the result of sequences of smaller, less significant steps. In the absence of results that are fully documented, reproducible, and generalizable, it becomes hard to re-use and extend these results. Besides hindering the ability of others to leverage our work, and consequently limiting the impact of our field, the absence of reproducibility experiments also puts our reputation at stake, since reliability and validity of empiric results are basic scientific principles. This seminar brought together experts from various sub-fields of computer science to create a joint understanding of the problems of reproducibility of experiments, discussing existing solutions and impediments, and proposing ways to overcome current limitations. The Neyman-Pearson (NP) approach to hypothesis testing is useful in situations where different types of error have different consequences or a priori probabilities are unknown. For any spl alpha >0, the NP lemma specifies the most powerful test of size spl alpha , but assumes the distributions for each hypothesis are known or (in some cases) the likelihood ratio is monotonic in an unknown parameter. This paper investigates an extension of NP theory to situations in which one has no knowledge of the underlying distributions except for a collection of independent and identically distributed (i.i.d.) training examples from each hypothesis. Building on a "fundamental lemma" of , we demonstrate that several concepts from statistical learning theory have counterparts in the NP context. Specifically, we consider constrained versions of empirical risk minimization (NP-ERM) and structural risk minimization (NP-SRM), and prove performance guarantees for both. General conditions are given under which NP-SRM leads to strong universal consistency. We also apply NP-SRM to (dyadic) decision trees to derive rates of convergence. Finally, we present explicit algorithms to implement NP-SRM for histograms and dyadic decision trees. As computational work becomes more and more integral to many aspects of scientific research, computational reproducibility has become an issue of increasing importance to computer systems researchers and domain scientists alike. Though computational reproducibility seems more straight forward than replicating physical experiments, the complex and rapidly changing nature of computer environments makes being able to reproduce and extend such work a serious challenge. In this paper, I explore common reasons that code developed for one research project cannot be successfully executed or extended by subsequent researchers. I review current approaches to these issues, including virtual machines and workflow systems, and their limitations. I then examine how the popular emerging technology Docker combines several areas from systems research - such as operating system virtualization, cross-platform portability, modular re-usable elements, versioning, and a 'DevOps' philosophy, to address these challenges. I illustrate this with several examples of Docker use with a focus on the R statistical environment.