MEDIRL: Predicting the Visual Attention of Drivers via Maximum Entropy Deep Inverse Reinforcement Learning

Author:

Baee Sonia1,Pakdamanian Erfan1,Kim Inki2,Feng Lu1,Ordonez Vicente3,Barnes Laura1

Affiliation:

1. University of Virginia

2. University of Illinois at Urbana Champaign

3. Rice University

Funder

National Science Foundation

Publisher

IEEE

Reference74 articles.

1. Video saliency detection via dynamic consistent spatio-temporal attention modelling;zhong;Twenty-Seventh AAAI Conference on Artificial Intelligence,2013

2. On learning intrinsic rewards for policy gradient methods;zheng;Advances in neural information processing systems,2018

3. AGIL: Learning Attention from Human for Visuomotor Tasks

4. Maximum entropy inverse reinforcement learning;ziebart;AAAI,2008

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