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Visual imitation learning enables reinforcement learning agents to learn to behave from expert visual demonstrations such as videos or image sequences, without explicit, well-defined rewards.
Efficient training of artificial neural networks for autonomous navigation
Dean A Pomerleau · 1991
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Apprenticeship learning using linear programming
Umar Syed, Michael Bowling, and Robert E Schapire · 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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Markov decision processes: discrete stochastic dynamic programming
Martin L Puterman · 2014
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Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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Generative adversarial imitation learning
Jonathan Ho and Stefano Ermon · 2016
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Precomputed real-time texture synthesis with markovian generative adversarial networks
Chuan Li and Michael Wand · 2016
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Dueling network architectures for deep reinforcement learning
Ziyu Wang, Tom Schaul, Matteo Hessel, Hado Hasselt, Marc Lanctot, and Nando Freitas · 2016
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Learning robust rewards with adversarial inverse reinforcement learning
Justin Fu, Katie Luo, and Sergey Levine · 2017
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Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates
Shixiang Gu, Ethan Holly, Timothy Lillicrap, and Sergey Levine · 2017
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Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
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Imitation learning: A survey of learning methods
Ahmed Hussein, Mohamed Medhat Gaber, Eyad Elyan, and Chrisina Jayne · 2017
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Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
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Mastering chess and shogi by self-play with a general reinforcement learning algorithm
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, et al · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
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Ilya Kostrikov, Kumar Krishna Agrawal, Debidatta Dwibedi, Sergey Levine, and Jonathan Tompson · 2018
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Zero-shot visual imitation
Deepak Pathak, Parsa Mahmoudieh, Guanghao Luo, Pulkit Agrawal, Dian Chen, Yide Shentu, Evan Shelhamer, Jitendra Malik, Alexei A Efros, and Trevor Darrell · 2018
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Yuval Tassa, Yotam Doron, Alistair Muldal, Tom Erez, Yazhe Li, Diego de Las Casas, David Budden, Abbas Abdolmaleki, Josh Merel, Andrew Lefrancq, et al · 2018
Stochastic latent actor-critic: Deep reinforcement learning with a latent variable model
Alex X Lee, Anusha Nagabandi, Pieter Abbeel, and Sergey Levine · 2020
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Image augmentation is all you need: Regularizing deep reinforcement learning from pixels
Denis Yarats, Ilya Kostrikov, and Rob Fergus · 2020
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Mastering complex control in moba games with deep reinforcement learning
Deheng Ye, Zhao Liu, Mingfei Sun, Bei Shi, Peilin Zhao, Hao Wu, Hongsheng Yu, Shaojie Yang, Xipeng Wu, Qingwei Guo, et al · 2020
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Smarts: Scalable multi-agent reinforcement learning training school for autonomous driving
Ming Zhou, Jun Luo, Julian Villella, Yaodong Yang, David Rusu, Jiayu Miao, Weinan Zhang, Montgomery Alban, Iman Fadakar, Zheng Chen, et al · 2020
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A survey of inverse reinforcement learning: Challenges, methods and progress
Saurabh Arora and Prashant Doshi · 2021
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Disagreement-regularized imitation learning
Kianté Brantley, Wen Sun, and Mikael Henaff · 2019
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Imitation learning via off-policy distribution matching
Ilya Kostrikov, Ofir Nachum, and Jonathan Tompson · 2019
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Sqil: Imitation learning via reinforcement learning with sparse rewards
Siddharth Reddy, Anca D Dragan, and Sergey Levine · 2019
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Adversarial imitation learning from state-only demonstrations
Faraz Torabi, Garrett Warnell, and Peter Stone · 2019
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Grandmaster level in starcraft ii using multi-agent reinforcement learning
Oriol Vinyals, Igor Babuschkin, Wojciech M Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H Choi, Richard Powell, Timo Ewalds, Petko Georgiev, et al · 2019
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Improving sample efficiency in model-free reinforcement learning from images
Denis Yarats, Amy Zhang, Ilya Kostrikov, Brandon Amos, Joelle Pineau, and Rob Fergus · 2019
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Randaugment: Practical automated data augmentation with a reduced search space
Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le · 2020
Cited alongside, same era.
Imitation learning from pixel observations for continuous control
Samuel Cohen, Brandon Amos, Marc Peter Deisenroth, Mikael Henaff, Eugene Vinitsky, and Denis Yarats · 2021
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Iq-learn: Inverse soft-q learning for imitation
Divyansh Garg, Shuvam Chakraborty, Chris Cundy, Jiaming Song, and Stefano Ermon · 2021
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Augmenting gail with bc for sample efficient imitation learning
Rohit Jena, Changliu Liu, and Katia Sycara · 2021
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Energy-based imitation learning
Minghuan Liu, Tairan He, Minkai Xu, and Weinan Zhang · 2021
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Visual adversarial imitation learning using variational models
Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, and Chelsea Finn · 2021
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Mastering visual continuous control: Improved data-augmented reinforcement learning
Denis Yarats, Rob Fergus, Alessandro Lazaric, and Lerrel Pinto · 2021
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Watch and match: Supercharging imitation with regularized optimal transport
Siddhant Haldar, Vaibhav Mathur, Denis Yarats, and Lerrel Pinto · 2022
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Plan your target and learn your skills: Transferable state-only imitation learning via decoupled policy optimization
Minghuan Liu, Zhengbang Zhu, Yuzheng Zhuang, Weinan Zhang, Jianye Hao, Yong Yu, and Jun Wang · 2022
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Perfectdou: Dominating doudizhu with perfect information distillation
Guan Yang, Minghuan Liu, Weijun Hong, Weinan Zhang, Fei Fang, Guangjun Zeng, and Yue Lin · 2022
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