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Found 4 papers, 1 papers with code
Leveraging Human Guidance for Deep Reinforcement Learning Tasks
Reinforcement learning agents can learn to solve sequential decision tasks by interacting with the environment.
Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset
We hope that the scale and quality of this dataset can provide more opportunities to researchers in the areas of visual attention, imitation learning, and reinforcement learning.
An initial attempt of combining visual selective attention with deep reinforcement learning
We visualize and analyze the feature maps of DQN on a toy problem Catch, and propose an approach to combine visual selective attention with deep reinforcement learning.
AGIL: Learning Attention from Human for Visuomotor Tasks
When intelligent agents learn visuomotor behaviors from human demonstrations, they may benefit from knowing where the human is allocating visual attention, which can be inferred from their gaze.
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