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Plasticity, the ability of a neural network to evolve with new data, is crucial for high-performance and sample-efficient visual reinforcement learning (VRL).
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Pablo Samuel Castro, Subhodeep Moitra, Carles Gelada, Saurabh Kumar, and Marc G. Bellemare · 2018
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Spectral normalization for generative adversarial networks
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Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Model based reinforcement learning for atari
Łukasz Kaiser, Mohammad Babaeizadeh, Piotr Miłos, Błażej Osiński, Roy H Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, et al · 2019
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Jordan Ash and Ryan P Adams · 2020
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Randomized ensembled double q-learning: Learning fast without a model
Xinyue Chen, Che Wang, Zijian Zhou, and Keith W Ross · 2020
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Revisiting fundamentals of experience replay
William Fedus, Prajit Ramachandran, Rishabh Agarwal, Yoshua Bengio, Hugo Larochelle, Mark Rowland, and Will Dabney · 2020
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Implicit under-parameterization inhibits data-efficient deep reinforcement learning
Aviral Kumar, Rishabh Agarwal, Dibya Ghosh, and Sergey Levine · 2020
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Predictive information accelerates learning in rl
Kuang-Huei Lee, Ian Fischer, Anthony Liu, Yijie Guo, Honglak Lee, John Canny, and Sergio Guadarrama · 2020
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Deep reinforcement and infomax learning
Bogdan Mazoure, Remi Tachet des Combes, Thang Long Doan, Philip Bachman, and R Devon Hjelm · 2020
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Data-efficient reinforcement learning with self-predictive representations
Max Schwarzer, Ankesh Anand, Rishab Goel, R Devon Hjelm, Aaron Courville, and Philip Bachman · 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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Continual backprop: Stochastic gradient descent with persistent randomness
Shibhansh Dohare, Richard S Sutton, and A Rupam Mahmood · 2021
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Highly accurate protein structure prediction with alphafold
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, et al · 2021
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Rrl: Resnet as representation for reinforcement learning
Rutav Shah and Vikash Kumar · 2021
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Masked visual pre-training for motor control
Tete Xiao, Ilija Radosavovic, Trevor Darrell, and Jitendra Malik · 2022
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Mask-based latent reconstruction for reinforcement learning
Tao Yu, Zhizheng Zhang, Cuiling Lan, Yan Lu, and Zhibo Chen · 2022
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Pre-trained image encoder for generalizable visual reinforcement learning
Zhecheng Yuan, Zhengrong Xue, Bo Yuan, Xueqian Wang, Yi Wu, Yang Gao, and Huazhe Xu · 2022
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Masked contrastive representation learning for reinforcement learning
Jinhua Zhu, Yingce Xia, Lijun Wu, Jiajun Deng, Wengang Zhou, Tao Qin, Tie-Yan Liu, and Houqiang Li · 2022
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Loss of plasticity in continual deep reinforcement learning
Zaheer Abbas, Rosie Zhao, Joseph Modayil, Adam White, and Marlos C Machado · 2023
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Tao Yu, Cuiling Lan, Wenjun Zeng, Mingxiao Feng, Zhizheng Zhang, and Zhibo Chen · 2021
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Stabilizing off-policy deep reinforcement learning from pixels
Edoardo Cetin, Philip J Ball, Stephen Roberts, and Oya Celiktutan · 2022
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Sample-efficient reinforcement learning by breaking the replay ratio barrier
Pierluca D’Oro, Max Schwarzer, Evgenii Nikishin, Pierre-Luc Bacon, Marc G Bellemare, and Aaron Courville · 2022
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Dribo: Robust deep reinforcement learning via multi-view information bottleneck
Jiameng Fan and Wenchao Li · 2022
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An empirical study of implicit regularization in deep offline rl
Caglar Gulcehre, Srivatsan Srinivasan, Jakub Sygnowski, Georg Ostrovski, Mehrdad Farajtabar, Matthew Hoffman, Razvan Pascanu, and Arnaud Doucet · 2022
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A comprehensive survey of data augmentation in visual reinforcement learning
Guozheng Ma, Zhen Wang, Zhecheng Yuan, Xueqian Wang, Bo Yuan, and Dacheng Tao · 2022
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The primacy bias in deep reinforcement learning
Evgenii Nikishin, Max Schwarzer, Pierluca D’Oro, Pierre-Luc Bacon, and Aaron Courville · 2022
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On pre-training for visuo-motor control: Revisiting a learning-from-scratch baseline
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Off-policy rl algorithms can be sample-efficient for continuous control via sample multiple reuse
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Learning better with less: Effective augmentation for sample-efficient visual reinforcement learning
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Deep reinforcement learning with plasticity injection
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