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Humans learn by interacting with their environments and perceiving theoutcomes of their actions. A landmark in artificial intelligence has been thedevelopment of deep reinforcement learning (dRL) algorithms capable of doingthe same in video games, on par with or better than humans. However, it remainsunclear whether the successes of dRL models reflect advances in visualrepresentation learning, the effectiveness of reinforcement learning algorithmsat discovering better policies, or both. To address this question, we introducethe Learning Challenge Diagnosticator (LCD), a tool that separately measuresthe perceptual and reinforcement learning demands of a task. We use LCD todiscover a novel taxonomy of challenges in the Procgen benchmark, anddemonstrate that these predictions are both highly reliable and can instructalgorithmic development. More broadly, the LCD reveals multiple failure casesthat can occur when optimizing dRL algorithms over entire video game benchmarkslike Procgen, and provides a pathway towards more efficient progress.

Diagnosing and exploiting the computational demands of videos games for deep reinforcement learning