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
翻译：最近几年,由于使用深层神经网络的方法取得成功,强化学习取得了长足的进步,但是,这种神经网络起到黑盒的作用,掩盖了内部运作。虽然强化学习有可能解决独特的问题,但缺乏信任和对强化学习算法的了解可能阻止其被广泛采用。在这里,我们提供一个图书馆,作为观察员,将“数据筛选器”附加给深层强化学习代理人,然后展示如何利用Atari数据采集器收集的数据来理解和解释深度强化学习代理人。Atari数据采集器的代码可以在这里找到:https://github.com/IRLL/Atari-Data-Scraper。
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