  PointGoal navigation has seen significant recent interest and progress,
spurred on by the Habitat platform and associated challenge. In this paper, we
study PointGoal navigation under both a sample budget (75 million frames) and a
compute budget (1 GPU for 1 day). We conduct an extensive set of experiments,
cumulatively totaling over 50,000 GPU-hours, that let us identify and discuss a
number of ostensibly minor but significant design choices -- the advantage
estimation procedure (a key component in training), visual encoder
architecture, and a seemingly minor hyper-parameter change. Overall, these
design choices to lead considerable and consistent improvements over the
baselines present in Savva et al. Under a sample budget, performance for RGB-D
agents improves 8 SPL on Gibson (14% relative improvement) and 20 SPL on
Matterport3D (38% relative improvement). Under a compute budget, performance
for RGB-D agents improves by 19 SPL on Gibson (32% relative improvement) and 35
SPL on Matterport3D (220% relative improvement). We hope our findings and
recommendations will make serve to make the community's experiments more
efficient.
