In recent years, significant progress has been made in solving challenging problems across various domains using deep reinforcement learning. However 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 discuss challenges that arise in experimental techniques and reporting procedures in deep RL, and will suggest methods and guidelines to make future results more reproducible, reusable and robust. I will also report on findings from the ICLR 2018 reproducibility challenge.