  Reinforcement learning has made great strides in recent years due to the
success of methods using deep neural networks. However, such neural networks
act as a black box, obscuring the inner workings. While reinforcement learning
has the potential to solve unique problems, a lack of trust and understanding
of reinforcement learning algorithms could prevent their widespread adoption.
Here, we present a library that attaches a "data scraper" to deep reinforcement
learning agents, acting as an observer, and then show how the data collected by
the Atari Data Scraper can be used to understand and interpret deep
reinforcement learning agents. The code for the Atari Data Scraper can be found
here: https://github.com/IRLL/Atari-Data-Scraper
