no code implementations • 21 Sep 2019 • Ruohan Zhang, Faraz Torabi, Lin Guan, Dana H. Ballard, Peter Stone
Reinforcement learning agents can learn to solve sequential decision tasks by interacting with the environment.
1 code implementation • 15 Mar 2019 • Ruohan Zhang, Calen Walshe, Zhuode Liu, Lin Guan, Karl S. Muller, Jake A. Whritner, Luxin Zhang, Mary M. Hayhoe, Dana H. Ballard
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.
no code implementations • 11 Nov 2018 • Liu Yuezhang, Ruohan Zhang, Dana H. Ballard
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.
no code implementations • ECCV 2018 • Ruohan Zhang, Zhuode Liu, Luxin Zhang, Jake A. Whritner, Karl S. Muller, Mary M. Hayhoe, Dana H. Ballard
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.