Despite recent incredible algorithmic advances in the field, deep reinforcement learning (DRL) remains notorious for being computationally expensive, prone to “silent bugs”, and difficult to tune hyperparameters. These phenomena make running high-fidelity, scientifically-rigorous reinforcement learning experiments paramount.

In this article, I will discuss a few tips and lessons I’ve learned to mitigate the effects of these difficulties in DRL — tips I never would have learned from a reinforcement learning class.  Read More