  Much human and computational effort has aimed to improve how deep
reinforcement learning algorithms perform on benchmarks such as the Atari
Learning Environment. Comparatively less effort has focused on understanding
what has been learned by such methods, and investigating and comparing the
representations learned by different families of reinforcement learning (RL)
algorithms. Sources of friction include the onerous computational requirements,
and general logistical and architectural complications for running Deep RL
algorithms at scale. We lessen this friction, by (1) training several
algorithms at scale and releasing trained models, (2) integrating with a
previous Deep RL model release, and (3) releasing code that makes it easy for
anyone to load, visualize, and analyze such models. This paper introduces the
Atari Zoo framework, which contains models trained across benchmark Atari
games, in an easy-to-use format, as well as code that implements common modes
of analysis and connects such models to a popular neural network visualization
library. Further, to demonstrate the potential of this dataset and software
package, we show initial quantitative and qualitative comparisons between the
performance and representations of several deep RL algorithms, highlighting
interesting and previously unknown distinctions between them.
