Reproducible, Reusable, and Robust Reinforcement Learning

We have seen significant achievements with deep reinforcement learning in recent years. Yet reproducing results for state-of-the-art deep RL methods is seldom straightforward. High variance of some methods can make learning particularly difficult when environments or rewards are strongly stochastic. Furthermore, results can be brittle to even minor perturbations in the domain or experimental procedure. In this talk, I will review challenges that arise in experimental techniques and reporting procedures in deep RL. I will also describe several recent results and guidelines designed to make future results more reproducible, reusable and robust.