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Learning-based approaches, such as reinforcement and imitation learning are gaining popularity in decision-making for autonomous driving.
“Observational Overfitting in Reinforcement Learning”, 2019
Xingyou Song, Yiding Jiang, Stephen Tu, Yilun Du and Behnam Neyshabur · 1912
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“Congested Traffic States in Empirical Observations and Microscopic Simulations”
Martin Treiber, Ansgar Hennecke and Dirk Helbing · 2000
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“Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor”
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel and Sergey Levine · 2003
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Patrick Hart and Alois Knoll · 2006
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“The Do-Calculus Revisited”, 2012
Judea Pearl · 2012
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“An introduction to counterfactual regret minimization”
Todd Neller and Marc Lanctot · 2013
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“Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving”, 2016
Shai Shalev-Shwartz, Shaked Shammah and Amnon Shashua · 2016
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“Generative adversarial imitation learning”
Jonathan Ho and Stefano Ermon · 2016
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“End to end learning for self-driving cars”
Mariusz Bojarski et al · 2016
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“Mobil: General lane-changing model for car-following models”
Martin Treiber and Dirk Helbing · 2016
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“Infogail: Interpretable imitation learning from visual demonstrations”
Yunzhu Li, Jiaming Song and Stefano Ermon · 2017
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“Generalization in deep learning”
Kenji Kawaguchi, Leslie Kaelbling and Yoshua Bengio · 2017
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“Chauffeurnet: Learning to drive by imitating the best and synthesizing the worst”
Mayank Bansal, Alex Krizhevsky and Abhijit Ogale · 2018
Cited alongside, same era.
“Quantifying Generalization in Reinforcement Learning”, 2018
Karl Cobbe, Oleg Klimov, Chris Hesse, Taehoon Kim and John Schulman · 2018
Later among the works it cites.
“The Book of Why: The New Science of Cause and Effect”
J Pearl and D Mackenzie · 2018
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“Woulda, coulda, shoulda: Counterfactually-guided policy search”
Lars Buesing et al · 2019
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“Interactive Decision Making for Autonomous Vehicles in Dense Traffic”
David Isele · 2019
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“Deep counterfactual regret minimization”
Noam Brown, Adam Lerer, Sam Gross and Tuomas Sandholm · 2019
Later among the works it cites.
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“End-to-end driving via conditional imitation learning”
Felipe Codevilla, Matthias Miiller, Antonio López, Vladlen Koltun and Alexey Dosovitskiy · 2018
Cited alongside, same era.
“A Study on Overfitting in Deep Reinforcement Learning”, 2018
Chiyuan Zhang, Oriol Vinyals, Remi Munos and Samy Bengio · 2018
Cited alongside, same era.
Patrick Hart, Leonard Rychly and Alois Knol · 2019
Later among the works it cites.
“Interpretable Machine Learning”
Christoph Molnar · 2020
Closest in time.