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We propose VRL3, a powerful data-driven framework with a simple design for solving challenging visual deep reinforcement learning (DRL) tasks.
Issues in using function approximation for reinforcement learning
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Chelsea Finn, Xin Yu Tan, Yan Duan, Trevor Darrell, Sergey Levine, and Pieter Abbeel · 2016
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Deep reinforcement learning with double q-learning
Hado Van Hasselt, Arthur Guez, and David Silver · 2016
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Reproducibility of benchmarked deep reinforcement learning tasks for continuous control
Riashat Islam, Peter Henderson, Maziar Gomrokchi, and Doina Precup · 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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Sample-efficient reinforcement learning with stochastic ensemble value expansion
Jacob Buckman, Danijar Hafner, George Tucker, Eugene Brevdo, and Honglak Lee · 2018
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Abhinav Gupta, Adithyavairavan Murali, Dhiraj Prakashchand Gandhi, and Lerrel Pinto · 2018
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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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Deep reinforcement learning that matters
Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger · 2018
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Model-ensemble trust-region policy optimization
Thanard Kurutach, Ignasi Clavera, Yan Duan, Aviv Tamar, and Pieter Abbeel · 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
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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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Variational recurrent models for solving partially observable control tasks
Dongqi Han, Kenji Doya, and Jun Tani · 2019
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When to trust your model: Model-based policy optimization
Michael Janner, Justin Fu, Marvin Zhang, and Sergey Levine · 2019
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Stabilizing off-policy q-learning via bootstrapping error reduction
Aviral Kumar, Justin Fu, George Tucker, and Sergey Levine · 2019
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Benchmarking model-based reinforcement learning
Eric Langlois, Shunshi Zhang, Guodong Zhang, Pieter Abbeel, and Jimmy Ba · 2019
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Advantage-weighted regression: Simple and scalable off-policy reinforcement learning
Xue Bin Peng, Aviral Kumar, Grace Zhang, and Sergey Levine · 2019
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Behavior regularized offline reinforcement learning
Yifan Wu, George Tucker, and Ofir Nachum · 2019
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Arthur Argenson and Gabriel Dulac-Arnold · 2020
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Bail: Best-action imitation learning for batch deep reinforcement learning
Xinyue Chen, Zijian Zhou, Zheng Wang, Che Wang, Yanqiu Wu, and Keith Ross · 2020
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Model-augmented actor-critic: Backpropagating through paths
Ignasi Clavera, Violet Fu, and Pieter Abbeel · 2020
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On the expressivity of neural networks for deep reinforcement learning
Kefan Dong, Yuping Luo, Tianhe Yu, Chelsea Finn, and Tengyu Ma · 2020
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Bootstrap your own latent-a new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al · 2020
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Never stop learning: The effectiveness of fine-tuning in robotic reinforcement learning
Ryan Julian, Benjamin Swanson, Gaurav S Sukhatme, Sergey Levine, Chelsea Finn, and Karol Hausman · 2020
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Rrl: Resnet as representation for reinforcement learning
Rutav Shah and Vikash Kumar · 2021
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Reinforcement learning with latent flow
Wenling Shang, Xiaofei Wang, Aravind Srinivas, Aravind Rajeswaran, Yang Gao, Pieter Abbeel, and Misha Laskin · 2021
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Uncertainty weighted actor-critic for offline reinforcement learning
Yue Wu, Shuangfei Zhai, Nitish Srivastava, Joshua Susskind, Jian Zhang, Ruslan Salakhutdinov, and Hanlin Goh · 2021
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Representation matters: Offline pretraining for sequential decision making
Mengjiao Yang and Ofir Nachum · 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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Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, and Thorsten Joachims · 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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Conservative q-learning for offline reinforcement learning
Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine · 2020
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Controlling overestimation bias with truncated mixture of continuous distributional quantile critics
Arsenii Kuznetsov, Pavel Shvechikov, Alexander Grishin, and Dmitry Vetrov · 2020
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Bidirectional model-based policy optimization
Hang Lai, Jian Shen, Weinan Zhang, and Yong Yu · 2020
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Maxmin q-learning: Controlling the estimation bias of q-learning
Qingfeng Lan, Yangchen Pan, Alona Fyshe, and Martha White · 2020
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Reinforcement learning with augmented data
Michael Laskin, Kimin Lee, Adam Stooke, Lerrel Pinto, Pieter Abbeel, and Aravind Srinivas · 2020
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Curl: Contrastive unsupervised representations for reinforcement learning
Michael Laskin, Aravind Srinivas, and Pieter Abbeel · 2020
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Image augmentation is all you need: Regularizing deep reinforcement learning from pixels
Denis Yarats, Ilya Kostrikov, and Rob Fergus · 2021
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Adaptive q-learning for interaction-limited reinforcement learning
Han Zheng, Xufang Luo, Xuan Song, Dongsheng Li, Jing Jiang, et al · 2021
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Pessimistic bootstrapping for uncertainty-driven offline reinforcement learning
Chenjia Bai, Lingxiao Wang, Zhuoran Yang, Zhihong Deng, Animesh Garg, Peng Liu, and Zhaoran Wang · 2022
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Offline-to-online reinforcement learning via balanced replay and pessimistic q-ensemble
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Does self-supervised learning really improve reinforcement learning from pixels?
Xiang Li, Jinghuan Shang, Srijan Das, and Michael S Ryoo · 2022
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Understanding and preventing capacity loss in reinforcement learning
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R3m: A universal visual representation for robot manipulation
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The primacy bias in deep reinforcement learning
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The unsurprising effectiveness of pre-trained vision models for control
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Visual Representation and Recognition without Human Supervision
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