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Large-scale public datasets have been shown to benefit research in multipleareas of modern artificial intelligence. For decision-making research thatrequires human data, high-quality datasets serve as important benchmarks tofacilitate the development of new methods by providing a common reproduciblestandard. Many human decision-making tasks require visual attention to obtainhigh levels of performance. Therefore, measuring eye movements can provide arich source of information about the strategies that humans use to solvedecision-making tasks. Here, we provide a large-scale, high-quality dataset ofhuman actions with simultaneously recorded eye movements while humans playAtari video games. The dataset consists of 117 hours of gameplay data from adiverse set of 20 games, with 8 million action demonstrations and 328 milliongaze samples. We introduce a novel form of gameplay, in which the human playsin a semi-frame-by-frame manner. This leads to near-optimal game decisions andgame scores that are comparable or better than known human records. Wedemonstrate the usefulness of the dataset through two simple applications:predicting human gaze and imitating human demonstrated actions. The quality ofthe data leads to promising results in both tasks. Moreover, using a learnedhuman gaze model to inform imitation learning leads to an 115\% increase ingame performance. We interpret these results as highlighting the importance ofincorporating human visual attention in models of decision making anddemonstrating the value of the current dataset to the research community. Wehope that the scale and quality of this dataset can provide more opportunitiesto researchers in the areas of visual attention, imitation learning, andreinforcement learning.

Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset