  In order to improve reproducibility, deep reinforcement learning (RL) has
been adopting better scientific practices such as standardized evaluation
metrics and reporting. However, the process of hyperparameter optimization
still varies widely across papers, which makes it challenging to compare RL
algorithms fairly. In this paper, we show that hyperparameter choices in RL can
significantly affect the agent's final performance and sample efficiency, and
that the hyperparameter landscape can strongly depend on the tuning seed which
may lead to overfitting. We therefore propose adopting established best
practices from AutoML, such as the separation of tuning and testing seeds, as
well as principled hyperparameter optimization (HPO) across a broad search
space. We support this by comparing multiple state-of-the-art HPO tools on a
range of RL algorithms and environments to their hand-tuned counterparts,
demonstrating that HPO approaches often have higher performance and lower
compute overhead. As a result of our findings, we recommend a set of best
practices for the RL community, which should result in stronger empirical
results with fewer computational costs, better reproducibility, and thus faster
progress. In order to encourage the adoption of these practices, we provide
plug-and-play implementations of the tuning algorithms used in this paper at
https://github.com/facebookresearch/how-to-autorl.
