Fetching the paper…
Reading the bibliography…
Deep neural networks are the most commonly used function approximators in offline reinforcement learning.
The interpretation of interaction in contingency tables
Edward H Simpson · 1951
Earlier work this paper cites.
ALVINN: An autonomous land vehicle in a neural network
Dean A Pomerleau · 1989
Earlier work this paper cites.
Linear least-squares algorithms for temporal difference learning
Steven Bradtke and Andrew Barto · 1996
Earlier work this paper cites.
Least-squares policy iteration
Michail G. Lagoudakis and Ronald Parr · 2003
Earlier work this paper cites.
Tree-based batch mode reinforcement learning
Damien Ernst, Pierre Geurts, and Louis Wehenkel · 2005
Earlier work this paper cites.
Neural fitted Q iteration – first experiences with a data efficient neural reinforcement learning method
Martin Riedmiller · 2005
Earlier work this paper cites.
Conservative q-learning for offline reinforcement learning
Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine · 2006
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
Earlier work this paper cites.
Implicit under-parameterization inhibits data-efficient deep reinforcement learning
Aviral Kumar, Rishabh Agarwal, Dibya Ghosh, and Sergey Levine · 2010
Earlier work this paper cites.
Deep sparse rectifier neural networks
Xavier Glorot, Antoine Bordes, and Yoshua Bengio · 2011
Earlier work this paper cites.
Batch reinforcement learning
Sascha Lange, Thomas Gabel, and Martin Riedmiller · 2012
Earlier work this paper cites.
Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
Tijmen Tieleman, Geoffrey Hinton, et al · 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
Earlier work this paper cites.
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
Yann Dauphin, Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, Surya Ganguli, and Yoshua Bengio · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Earlier work this paper cites.
Domain-independent optimistic initialization for reinforcement learning
Marlos C Machado, Sriram Srinivasan, and Michael Bowling · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
Charles Beattie, Joel Z Leibo, Denis Teplyashin, Tom Ward, Marcus Wainwright, Heinrich Küttler, Andrew Lefrancq, Simon Green, Víctor Valdés, Amir Sadik, et al · 2016
Earlier work this paper cites.
Unifying count-based exploration and intrinsic motivation
Marc Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, and Remi Munos · 2016
Earlier work this paper cites.
Noisy activation functions
Caglar Gulcehre, Marcin Moczulski, Misha Denil, and Yoshua Bengio · 2016
Earlier work this paper cites.
On large-batch training for deep learning: Generalization gap and sharp minima
Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang · 2016
Earlier work this paper cites.
Singularity of the hessian in deep learning
Levent Sagun, Léon Bottou, and Yann LeCun · 2016
Earlier work this paper cites.
Deep reinforcement learning with double Q-learning
Hado Van Hasselt, Arthur Guez, and David Silver · 2016
Earlier work this paper cites.
Rethinking atrous convolution for semantic image segmentation
Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam · 2017
Earlier work this paper cites.
Sharp minima can generalize for deep nets
Laurent Dinh, Razvan Pascanu, Samy Bengio, and Yoshua Bengio · 2017
Cited alongside, same era.
Visualizing the loss landscape of neural nets
Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, and Tom Goldstein · 2017
Cited alongside, same era.
Nonlinear random matrix theory for deep learning
Jeffrey Pennington and Pratik Worah · 2017
Cited alongside, same era.
Empirical analysis of the hessian of over-parametrized neural networks
Levent Sagun, Utku Evci, V Ugur Guney, Yann Dauphin, and Leon Bottou · 2017
Cited alongside, same era.
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, and Adrian Bolton · 2017
Cited alongside, same era.
RL unplugged: Benchmarks for offline reinforcement learning
Caglar Gulcehre, Ziyu Wang, Alexander Novikov, Tom Le Paine, Sergio Gómez Colmenarejo, Konrad Zolna, Rishabh Agarwal, Josh Merel, Daniel Mankowitz, Cosmin Paduraru, et al · 2020
Later among the works it cites.
Haiku: Sonnet for JAX, 2020
Tom Hennigan, Trevor Cai, Tamara Norman, and Igor Babuschkin · 2020
Later among the works it cites.
Optax: composable gradient transformation and optimisation, in jax!, 2020
Matteo Hessel, David Budden, Fabio Viola, Mihaela Rosca, Eren Sezener, and Tom Hennigan · 2020
Later among the works it cites.
Acme: A research framework for distributed reinforcement learning
Matt Hoffman, Bobak Shahriari, John Aslanides, Gabriel Barth-Maron, Feryal Behbahani, Tamara Norman, Abbas Abdolmaleki, Albin Cassirer, Fan Yang, Kate Baumli, Sarah Henderson, Alex Novikov, Sergio Gómez Colmenarejo, Serkan Cabi, Caglar Gulcehre, Tom Le Paine, Andrew Cowie, Ziyu Wang, Bilal Piot, and Nando de Freitas · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Samuel L Smith and Quoc V Le · 2017
Cited alongside, same era.
Image augmentation is all you need: Regularizing deep reinforcement learning from pixels
Denis Yarats, Ilya Kostrikov, and Rob Fergus · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Addressing function approximation error in actor-critic methods
Scott Fujimoto, Herke Van Hoof, and David Meger · 2018
Cited alongside, same era.
Learning overparameterized neural networks via stochastic gradient descent on structured data
Yuanzhi Li and Yingyu Liang · 2018
Cited alongside, same era.
Robustness via deep low-rank representations
Amartya Sanyal, Varun Kanade, Philip HS Torr, and Puneet K Dokania · 2018
Cited alongside, same era.
Implicit regularization in deep matrix factorization
Sanjeev Arora, Nadav Cohen, Wei Hu, and Yuping Luo · 2019
Cited alongside, same era.
Kaixuan Huang, Yuqing Wang, Molei Tao, and Tuo Zhao · 2020
Later among the works it cites.
Transient non-stationarity and generalisation in deep reinforcement learning
Maximilian Igl, Gregory Farquhar, Jelena Luketina, Wendelin Boehmer, and Shimon Whiteson · 2020
Later among the works it cites.
Curl: Contrastive unsupervised representations for reinforcement learning
Michael Laskin, Aravind Srinivas, and Pieter Abbeel · 2020
Later among the works it cites.
Offline reinforcement learning: Tutorial, review, and perspectives on open problems
Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu · 2020
Later among the works it cites.
Self-distillation amplifies regularization in Hilbert space
Hossein Mobahi, Mehrdad Farajtabar, and Peter L Bartlett · 2020
Later among the works it cites.
Hyperparameter selection for offline reinforcement learning
Tom Le Paine, Cosmin Paduraru, Andrea Michi, Caglar Gulcehre, Konrad Zolna, Alexander Novikov, Ziyu Wang, and Nando de Freitas · 2020
Later among the works it cites.
Trainability of relu networks and data-dependent initialization
Yeonjong Shin and George Em Karniadakis · 2020
Later among the works it cites.
Deep reinforcement learning at the edge of the statistical precipice
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C Courville, and Marc Bellemare · 2021
Later among the works it cites.
Sharpness-aware minimization for efficiently improving generalization
Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur · 2021
Later among the works it cites.
Regularized behavior value estimation
Caglar Gulcehre, Sergio Gómez Colmenarejo, Ziyu Wang, Jakub Sygnowski, Thomas Paine, Konrad Zolna, Yutian Chen, Matthew Hoffman, Razvan Pascanu, and Nando de Freitas · 2021
Later among the works it cites.
The low-rank simplicity bias in deep networks
Minyoung Huh, Hossein Mobahi, Richard Zhang, Brian Cheung, Pulkit Agrawal, and Phillip Isola · 2021
Later among the works it cites.
Is pessimism provably efficient for offline RL?
Ying Jin, Zhuoran Yang, and Zhaoran Wang · 2021
Later among the works it cites.
Active offline policy selection
Ksenia Konyushkova, Yutian Chen, Thomas Paine, Caglar Gulcehre, Cosmin Paduraru, Daniel J Mankowitz, Misha Denil, and Nando de Freitas · 2021
Later among the works it cites.
On the effect of auxiliary tasks on representation dynamics
Clare Lyle, Mark Rowland, Georg Ostrovski, and Will Dabney · 2021
Later among the works it cites.
Implicit self-regularization in deep neural networks: Evidence from random matrix theory and implications for learning
Charles H Martin and Michael W Mahoney · 2021
Later among the works it cites.
Starcraft ii unplugged: Large scale offline reinforcement learning
Michael Mathieu, Sherjil Ozair, Srivatsan Srinivasan, Caglar Gulcehre, Shangtong Zhang, Ray Jiang, Tom Le Paine, Konrad Zolna, Richard Powell, Julian Schrittwieser, et al · 2021
Later among the works it cites.
The difficulty of passive learning in deep reinforcement learning
Georg Ostrovski, Pablo Samuel Castro, and Will Dabney · 2021
Later among the works it cites.
Offline reinforcement learning for autonomous driving with safety and exploration enhancement
Tianyu Shi, Dong Chen, Kaian Chen, and Zhaojian Li · 2021
Later among the works it cites.
On the origin of implicit regularization in stochastic gradient descent
Samuel L Smith, Benoit Dherin, David GT Barrett, and Soham De · 2021
Later among the works it cites.
Bellman-consistent pessimism for offline reinforcement learning
Tengyang Xie, Ching-An Cheng, Nan Jiang, Paul Mineiro, and Alekh Agarwal · 2021
Later among the works it cites.
Teaching language models to support answers with verified quotes
Jacob Menick, Maja Trebacz, Vladimir Mikulik, John Aslanides, Francis Song, Martin Chadwick, Mia Glaese, Susannah Young, Lucy Campbell-Gillingham, Geoffrey Irving, and Nat McAleese · 2022
Closest in time.