Title: How Many Random Seeds? Statistical Power Analysis in Deep Reinforcement Learning Experiments,
Abstract: Consistently checking the statistical significance of experimental results is
one of the mandatory methodological steps to address the so-called
"reproducibility crisis" in deep reinforcement learning. In this tutorial
paper, we explain how the number of random seeds relates to the probabilities
of statistical errors. For both the t-test and the bootstrap confidence
interval test, we recall theoretical guidelines to determine the number of
random seeds one should use to provide a statistically significant comparison
of the performance of two algorithms. Finally, we discuss the influence of
deviations from the assumptions usually made by statistical tests. We show that
they can lead to inaccurate evaluations of statistical errors and provide
guidelines to counter these negative effects. We make our code available to
perform the tests.