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Value functions are a central component of deep reinforcement learning (RL).
Rule-based machine learning methods for functional prediction
Sholom M Weiss and Nitin Indurkhya · 1995
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Regression by classification
Luís Torgo and João Gama · 1996
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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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, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna · 2016
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Pixel recurrent neural networks
Aäron Van Den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu · 2016
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A distributional perspective on reinforcement learning
Marc G. Bellemare, Will Dabney, and Rémi Munos · 2017
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End-to-end learning of geometry and context for deep stereo regression
Alex Kendall, Hayk Martirosyan, Saumitro Dasgupta, Peter Henry, Ryan Kennedy, Abraham Bachrach, and Adam Bry · 2017
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Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, Yutian Chen, Timothy Lillicrap, Fan Hui, Laurent Sifre, George van den Driessche, Thore Graepel, and Demis Hassabis · 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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JAX: composable transformations of Python+NumPy programs, 2018
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang · 2018
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Dopamine: A Research Framework for Deep Reinforcement Learning
Pablo Samuel Castro, Subhodeep Moitra, Carles Gelada, Saurabh Kumar, and Marc G. Bellemare · 2018
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Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures
Lasse Espeholt, Hubert Soyer, Remi Munos, Karen Simonyan, Vlad Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, Shane Legg, and Koray Kavukcuoglu · 2018
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Generalization and regularization in DQN
Jesse Farebrother, Marlos C. Machado, and Michael Bowling · 2018
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Improving regression performance with distributional losses
Ehsan Imani and Martha White · 2018
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Revisiting the arcade learning environment: Evaluation protocols and open problems for general agents
Marlos C. Machado, Marc G. Bellemare, Erik Talvitie, Joel Veness, Matthew Hausknecht, and Michael Bowling · 2018
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Deep expectation of real and apparent age from a single image without facial landmarks
Rasmus Rothe, Radu Timofte, and Luc Van Gool · 2018
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A geometric perspective on optimal representations for reinforcement learning
Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taïga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, and Clare Lyle · 2019
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A comparative analysis of expected and distributional reinforcement learning
Clare Lyle, Marc G. Bellemare, and Pablo Samuel Castro · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Lcr-net++: Multi-person 2d and 3d pose detection in natural images
Gregory Rogez, Philippe Weinzaepfel, and Cordelia Schmid · 2019
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An optimistic perspective on offline reinforcement learning
Rishabh Agarwal, Dale Schuurmans, and Mohammad Norouzi · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Investigating multi-task pretraining and generalization in reinforcement learning
Adrien Ali Taïga, Rishabh Agarwal, Jesse Farebrother, Aaron Courville, and Marc G. Bellemare · 2023
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Distributional reinforcement learning
Marc G. Bellemare, Will Dabney, and Mark Rowland · 2023
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Q-transformer: Scalable offline reinforcement learning via autoregressive q-functions
Yevgen Chebotar, Quan Vuong, Karol Hausman, Fei Xia, Yao Lu, Alex Irpan, Aviral Kumar, Tianhe Yu, Alexander Herzog, Karl Pertsch, et al · 2023
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Proto-value networks: Scaling representation learning with auxiliary tasks
Jesse Farebrother, Joshua Greaves, Rishabh Agarwal, Charline Le Lan, Ross Goroshin, Pablo Samuel Castro, and Marc G. Bellemare · 2023
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Mastering diverse domains through world models
Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, and Timothy P. Lillicrap · 2023
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Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 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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Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems
Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu · 2020
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Mastering atari, go, chess and shogi by planning with a learned model
Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, Timothy P. Lillicrap, and David Silver · 2020
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Deep reinforcement learning at the edge of the statistical precipice
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron Courville, and Marc G. Bellemare · 2021
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Revisiting rainbow: Promoting more insightful and inclusive deep reinforcement learning research
Johan Samir Obando Ceron and Pablo Samuel Castro · 2021
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The value-improvement path: Towards better representations for reinforcement learning
Will Dabney, André Barreto, Mark Rowland, Robert Dadashi, John Quan, Marc G. Bellemare, and David Silver · 2021
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Offline Q-Learning on Diverse Multi-Task Data Both Scales and Generalizes
Aviral Kumar, Rishabh Agarwal, Xinyang Geng, George Tucker, and Sergey Levine · 2023
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Bootstrapped representations in reinforcement learning
Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G. Bellemare, and Will Dabney · 2023
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A step towards understanding why classification helps regression
Silvia L. Pintea, Yancong Lin, Jouke Dijkstra, and Jan C. van Gemert · 2023
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The statistical benefits of quantile temporal-difference learning for value estimation
Mark Rowland, Yunhao Tang, Clare Lyle, Rémi Munos, Marc G. Bellemare, and Will Dabney · 2023
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Offline RL for natural language generation with implicit language q learning
Charlie Victor Snell, Ilya Kostrikov, Yi Su, Sherry Yang, and Sergey Levine · 2023
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Regression as classification: Influence of task formulation on neural network features
Lawrence Stewart, Francis Bach, Quentin Berthet, and Jean-Philippe Vert · 2023
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The benefits of being distributional: Small-loss bounds for reinforcement learning
Kaiwen Wang, Kevin Zhou, Runzhe Wu, Nathan Kallus, and Wen Sun · 2023
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Improving deep regression with ordinal entropy
Shihao Zhang, Linlin Yang, Michael Bi Mi, Xiaoxu Zheng, and Angela Yao · 2023
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TD-MPC2: Scalable, robust world models for continuous control
Nicklas Hansen, Hao Su, and Xiaolong Wang · 2024
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Investigating the histogram loss in regression
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Beyond a*: Better planning with transformers via search dynamics bootstrapping
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Disentangling the causes of plasticity loss in neural networks
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Mixtures of experts unlock parameter scaling for deep rl
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From sparse to soft mixtures of experts
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Grandmaster-level chess without search
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Offline actor-critic reinforcement learning scales to large models
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