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Learning-based vehicle planning is receiving increasing attention with the emergence of diverse driving simulators and large-scale driving datasets.
Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning
Richard S Sutton, Doina Precup, and Satinder Singh · 1999
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The construction of movement with behavior-specific and behavior-independent modules
Jian Jing, Elizabeth C Cropper, Itay Hurwitz, and Klaudiusz R Weiss · 2004
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
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A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey Gordon, and Drew Bagnell · 2011
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On the properties of neural machine translation: Encoder-decoder approaches
Kyunghyun Cho, Bart Van Merriënboer, Dzmitry Bahdanau, and Yoshua Bengio · 2014
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Trajectory planning for bertha—a local, continuous method
Julius Ziegler, Philipp Bender, Thao Dang, and Christoph Stiller · 2014
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Markov decision processes: discrete stochastic dynamic programming
Martin L Puterman · 2014
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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2016
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Carla: An open urban driving simulator
Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun · 2017
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Hindsight experience replay
Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, OpenAI Pieter Abbeel, and Wojciech Zaremba · 2017
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On the quantitative analysis of decoder-based generative models
Yuhuai Wu, Yuri Burda, Ruslan Salakhutdinov, and Roger Grosse · 2017
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beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
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Advances in variational inference
Cheng Zhang, Judith Bütepage, Hedvig Kjellström, and Stephan Mandt · 2018
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Understanding disentangling in b e t a beta -vae
Christopher P Burgess, Irina Higgins, Arka Pal, Loic Matthey, Nick Watters, Guillaume Desjardins, and Alexander Lerchner · 2018
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Chauffeurnet: Learning to drive by imitating the best and synthesizing the worst
Mayank Bansal, Alex Krizhevsky, and Abhijit Ogale · 2018
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How would surround vehicles move? a unified framework for maneuver classification and motion prediction
Nachiket Deo, Akshay Rangesh, and Mohan M Trivedi · 2018
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Exponentially weighted imitation learning for batched historical data
Qing Wang, Jiechao Xiong, Lei Han, Han Liu, Tong Zhang, et al · 2018
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Off-policy deep reinforcement learning without exploration
Scott Fujimoto, David Meger, and Doina Precup · 2019
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Neural state machine for character-scene interactions
Sebastian Starke, He Zhang, Taku Komura, and Jun Saito · 2019
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Autonomous driving motion planning with constrained iterative lqr
Jianyu Chen, Wei Zhan, and Masayoshi Tomizuka · 2019
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Exploring data aggregation in policy learning for vision-based urban autonomous driving
Aditya Prakash, Aseem Behl, Eshed Ohn-Bar, Kashyap Chitta, and Andreas Geiger · 2020
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Offline reinforcement learning: Tutorial, review, and perspectives on open problems
Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu · 2020
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Morel: Model-based offline reinforcement learning
Goal-conditioned reinforcement learning with imagined subgoals
Elliot Chane-Sane, Cordelia Schmid, and Ivan Laptev · 2021
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Accelerating reinforcement learning with learned skill priors
Karl Pertsch, Youngwoon Lee, and Joseph Lim · 2021
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Opal: Offline primitive discovery for accelerating offline reinforcement learning
Anurag Ajay, Aviral Kumar, Pulkit Agrawal, Sergey Levine, and Ofir Nachum · 2021
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Offline reinforcement learning with implicit q-learning
Ilya Kostrikov, Ashvin Nair, and Sergey Levine · 2021
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End-to-end urban driving by imitating a reinforcement learning coach
Zhejun Zhang, Alexander Liniger, Dengxin Dai, Fisher Yu, and Luc Van Gool · 2021
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Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, and Thorsten Joachims · 2020
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Learning latent plans from play
Corey Lynch, Mohi Khansari, Ted Xiao, Vikash Kumar, Jonathan Tompson, Sergey Levine, and Pierre Sermanet · 2020
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Nvae: A deep hierarchical variational autoencoder
Arash Vahdat and Jan Kautz · 2020
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Conservative q-learning for offline reinforcement learning
Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine · 2020
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Iris: Implicit reinforcement without interaction at scale for learning control from offline robot manipulation data
Ajay Mandlekar, Fabio Ramos, Byron Boots, Silvio Savarese, Li Fei-Fei, Animesh Garg, and Dieter Fox · 2020
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The k-means algorithm: A comprehensive survey and performance evaluation
Mohiuddin Ahmed, Raihan Seraj, and Syed Mohammed Shamsul Islam · 2020
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Carla autonomous driving leaderboard
Carla team · 2020
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Scott Fujimoto and Shixiang Shane Gu · 2021
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Uncertainty weighted actor-critic for offline reinforcement learning
Yue Wu, Shuangfei Zhai, Nitish Srivastava, Joshua M Susskind, Jian Zhang, Ruslan Salakhutdinov, and Hanlin Goh · 2021
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Intersim: Interactive traffic simulation via explicit relation modeling
Qiao Sun, Xin Huang, Brian C Williams, and Hang Zhao · 2022
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Hierarchical planning through goal-conditioned offline reinforcement learning
Jinning Li, Chen Tang, Masayoshi Tomizuka, and Wei Zhan · 2022
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Tong Zhou, Letian Wang, Ruobing Chen, Wenshuo Wang, and Yu Liu · 2022
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Hierarchical model-based imitation learning for planning in autonomous driving
Eli Bronstein, Mark Palatucci, Dominik Notz, Brandyn White, Alex Kuefler, Yiren Lu, Supratik Paul, Payam Nikdel, Paul Mougin, Hongge Chen, et al · 2022
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Transferable and adaptable driving behavior prediction
Letian Wang, Yeping Hu, Liting Sun, Wei Zhan, Masayoshi Tomizuka, and Changliu Liu · 2022
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Rethinking closed-loop training for autonomous driving
Chris Zhang, Runsheng Guo, Wenyuan Zeng, Yuwen Xiong, Binbin Dai, Rui Hu, Mengye Ren, and Raquel Urtasun · 2022
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Model-based imitation learning for urban driving
Anthony Hu, Gianluca Corrado, Nicolas Griffiths, Zachary Murez, Corina Gurau, Hudson Yeo, Alex Kendall, Roberto Cipolla, and Jamie Shotton · 2022
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Skill-based meta-reinforcement learning
Taewook Nam, Shao-Hua Sun, Karl Pertsch, Sung Ju Hwang, and Joseph J Lim · 2022
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Latent plans for task-agnostic offline reinforcement learning
Erick Rosete-Beas, Oier Mees, Gabriel Kalweit, Joschka Boedecker, and Wolfram Burgard · 2022
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