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Visual Reinforcement Learning (Visual RL), coupled with high-dimensional observations, has consistently confronted the long-standing challenge of out-of-distribution generalization.
Operational space control: A theoretical and empirical comparison
Jun Nakanishi, Rick Cory, Michael Mistry, Jan Peters, and Stefan Schaal · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
Earlier work this paper cites.
The arcade learning environment: An evaluation platform for general agents
Marc G Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2013
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
Earlier work this paper cites.
Benchmarking deep reinforcement learning for continuous control
Yan Duan, Xi Chen, Rein Houthooft, John Schulman, and Pieter Abbeel · 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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Learning complex dexterous manipulation with deep reinforcement learning and demonstrations
Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, Giulia Vezzani, John Schulman, Emanuel Todorov, and Sergey Levine · 2017
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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 · 2018
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Benchmarking Safe Exploration in Deep Reinforcement Learning
Alex Ray, Joshua Achiam, and Dario Amodei · 2019
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Habitat: A Platform for Embodied AI Research
Manolis Savva, Abhishek Kadian, Oleksandr Maksymets, Yili Zhao, Erik Wijmans, Bhavana Jain, Julian Straub, Jia Liu, Vladlen Koltun, Jitendra Malik, Devi Parikh, and Dhruv Batra · 2019
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Benchmarking model-based reinforcement learning
Tingwu Wang, Xuchan Bao, Ignasi Clavera, Jerrick Hoang, Yeming Wen, Eric Langlois, Shunshi Zhang, Guodong Zhang, Pieter Abbeel, and Jimmy Ba · 2019
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Leveraging procedural generation to benchmark reinforcement learning
Karl Cobbe, Chris Hesse, Jacob Hilton, and John Schulman · 2020
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Ultra: A reinforcement learning generalization benchmark for autonomous driving, 2020
Mohamed Elsayed, Kimia Hassanzadeh, Nhat M Nguyen, Montgomery Alban, Xiru Zhu, Daniel Graves, and Jun Luo · 2020
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D4rl: Datasets for deep data-driven reinforcement learning
Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine · 2020
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Image augmentation is all you need: Regularizing deep reinforcement learning from pixels
Ilya Kostrikov, Denis Yarats, and Rob Fergus · 2020
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dm_control: Software and tasks for continuous control
Saran Tunyasuvunakool, Alistair Muldal, Yotam Doron, Siqi Liu, Steven Bohez, Josh Merel, Tom Erez, Timothy Lillicrap, Nicolas Heess, and Yuval Tassa · 2020
Earlier work this paper cites.
SAPIEN: A simulated part-based interactive environment
Fanbo Xiang, Yuzhe Qin, Kaichun Mo, Yikuan Xia, Hao Zhu, Fangchen Liu, Minghua Liu, Hanxiao Jiang, Yifu Yuan, He Wang, Li Yi, Angel X. Chang, Leonidas J. Guibas, and Hao Su · 2020
Cited alongside, same era.
Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Karol Hausman, Chelsea Finn, and Sergey Levine · 2020
Cited alongside, same era.
Learning invariant representations for reinforcement learning without reconstruction
Amy Zhang, Rowan McAllister, Roberto Calandra, Yarin Gal, and Sergey Levine · 2020
Cited alongside, same era.
robosuite: A modular simulation framework and benchmark for robot learning
Yuke Zhu, Josiah Wong, Ajay Mandlekar, Roberto Martín-Martín, Abhishek Joshi, Soroush Nasiriany, and Yifeng Zhu · 2020
Cited alongside, same era.
Secant: Self-expert cloning for zero-shot generalization of visual policies
MuJoCo Menagerie: A collection of high-quality simulation models for MuJoCo, 2022
MuJoCo Menagerie Contributors · 2022
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Minedojo: Building open-ended embodied agents with internet-scale knowledge
Linxi Fan, Guanzhi Wang, Yunfan Jiang, Ajay Mandlekar, Yuncong Yang, Haoyi Zhu, Andrew Tang, De-An Huang, Yuke Zhu, and Anima Anandkumar · 2022
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Carlaenv-benchmark, 2022
Yangru Huang Haoran Xu, Ding Chen · 2022
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Spectrum random masking for generalization in image-based reinforcement learning
Yangru Huang, Peixi Peng, Yifan Zhao, Guangyao Chen, and Yonghong Tian · 2022
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Does self-supervised learning really improve reinforcement learning from pixels?
Xiang Li, Jinghuan Shang, Srijan Das, and Michael Ryoo · 2022
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Training language models to follow instructions with human feedback
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Linxi Fan, Guanzhi Wang, De-An Huang, Zhiding Yu, Li Fei-Fei, Yuke Zhu, and Animashree Anandkumar · 2021
Cited alongside, same era.
Generalization in reinforcement learning by soft data augmentation
Nicklas Hansen and Xiaolong Wang · 2021
Cited alongside, same era.
Stabilizing deep q-learning with convnets and vision transformers under data augmentation
Nicklas Hansen, Hao Su, and Xiaolong Wang · 2021
Cited alongside, same era.
Goal-aware cross-entropy for multi-target reinforcement learning
Kibeom Kim, Min Whoo Lee, Yoonsung Kim, JeHwan Ryu, Minsu Lee, and Byoung-Tak Zhang · 2021
Cited alongside, same era.
A survey of generalisation in deep reinforcement learning
Robert Kirk, Amy Zhang, Edward Grefenstette, and Tim Rocktäschel · 2021
Cited alongside, same era.
Wilds: A benchmark of in-the-wild distribution shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, et al · 2021
Cited alongside, same era.
Urlb: Unsupervised reinforcement learning benchmark
Michael Laskin, Denis Yarats, Hao Liu, Kimin Lee, Albert Zhan, Kevin Lu, Catherine Cang, Lerrel Pinto, and Pieter Abbeel · 2021
Cited alongside, same era.
Maniskill: Generalizable manipulation skill benchmark with large-scale demonstrations
Tongzhou Mu, Zhan Ling, Fanbo Xiang, Derek Cathera Yang, Xuanlin Li, Stone Tao, Zhiao Huang, Zhiwei Jia, and Hao Su · 2021
Cited alongside, same era.
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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Vrl3: A data-driven framework for visual deep reinforcement learning
Che Wang, Xufang Luo, Keith Ross, and Dongsheng Li · 2022
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Assaying out-of-distribution generalization in transfer learning
Florian Wenzel, Andrea Dittadi, Peter Gehler, Carl-Johann Simon-Gabriel, Max Horn, Dominik Zietlow, David Kernert, Chris Russell, Thomas Brox, Bernt Schiele, et al · 2022
Later among the works it cites.
Zhengyu Yang, Kan Ren, Xufang Luo, Minghuan Liu, Weiqing Liu, Jiang Bian, Weinan Zhang, and Dongsheng Li · 2022
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Ood-bench: Quantifying and understanding two dimensions of out-of-distribution generalization
Nanyang Ye, Kaican Li, Haoyue Bai, Runpeng Yu, Lanqing Hong, Fengwei Zhou, Zhenguo Li, and Jun Zhu · 2022
Later among the works it cites.
Daxbench: Benchmarking deformable object manipulation with differentiable physics
Siwei Chen, Yiqing Xu, Cunjun Yu, Linfeng Li, Xiao Ma, Zhongwen Xu, and David Hsu · 2023
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Dense reinforcement learning for safety validation of autonomous vehicles
Shuo Feng, Haowei Sun, Xintao Yan, Haojie Zhu, Zhengxia Zou, Shengyin Shen, and Henry X Liu · 2023
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Maniskill2: A unified benchmark for generalizable manipulation skills
Jiayuan Gu, Fanbo Xiang, Xuanlin Li, Zhan Ling, Xiqiaing Liu, Tongzhou Mu, Yihe Tang, Stone Tao, Xinyue Wei, Yunchao Yao, Xiaodi Yuan, Pengwei Xie, Zhiao Huang, Rui Chen, and Hao Su · 2023
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A survey of zero-shot generalisation in deep reinforcement learning
Robert Kirk, Amy Zhang, Edward Grefenstette, and Tim Rocktäschel · 2023
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Where are we in the search for an artificial visual cortex for embodied intelligence?
Arjun Majumdar, Karmesh Yadav, Sergio Arnaud, Yecheng Jason Ma, Claire Chen, Sneha Silwal, Aryan Jain, Vincent-Pierre Berges, Pieter Abbeel, Jitendra Malik, et al · 2023
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Decomposing the generalization gap in imitation learning for visual robotic manipulation
Annie Xie, Lisa Lee, Ted Xiao, and Chelsea Finn · 2023
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What makes representation learning from videos hard for control?
Tony Z. Zhao, Siddharth Karamcheti, Kollar Thomas, Chelsea Finn, and Liang Percy · 2023
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