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Much human and computational effort has aimed to improve how deepreinforcement learning algorithms perform on benchmarks such as the AtariLearning Environment. Comparatively less effort has focused on understandingwhat has been learned by such methods, and investigating and comparing therepresentations 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 RLalgorithms at scale. We lessen this friction, by (1) training severalalgorithms at scale and releasing trained models, (2) integrating with aprevious Deep RL model release, and (3) releasing code that makes it easy foranyone to load, visualize, and analyze such models. This paper introduces theAtari Zoo framework, which contains models trained across benchmark Atarigames, in an easy-to-use format, as well as code that implements common modesof analysis and connects such models to a popular neural network visualizationlibrary. Further, to demonstrate the potential of this dataset and softwarepackage, we show initial quantitative and qualitative comparisons between theperformance and representations of several deep RL algorithms, highlightinginteresting and previously unknown distinctions between them.

An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents