Abstract:
We propose a framework that uses learned human visual attention model to guide the learning process of an imitation learning or reinforcement learning agent. We have collected high-quality human action and eye-tracking data while playing Atari games in a carefully controlled experimental setting. We have shown that incorporating a learned human gaze model into deep imitation learning yields promising results.
DOI:
10.1609/aaai.v33i01.33019906