Fetching the paper…
Reading the bibliography…
In this paper, we study the dynamics of temporal difference learning with neural network-based value function approximation over a general state space, namely, \emph{Neural TD learning}.
Probability with martingales
David Williams · 1991
Earlier work this paper cites.
An analysis of temporal-difference learning with function approximation
John N Tsitsiklis and Benjamin Van Roy · 1997
Earlier work this paper cites.
Maximum-margin matrix factorization
Nathan Srebro, Jason Rennie, and Tommi S Jaakkola · 2005
Earlier work this paper cites.
Rank, trace-norm and max-norm
Nathan Srebro and Adi Shraibman · 2005
Earlier work this paper cites.
Universal kernels
Charles A Micchelli, Yuesheng Xu, and Haizhang Zhang · 2006
Earlier work this paper cites.
Large deviations of vector-valued martingales in 2-smooth normed spaces
Anatoli Juditsky and Arkadii S Nemirovski · 2008
Earlier work this paper cites.
Uniform approximation of functions with random bases
Ali Rahimi and Benjamin Recht · 2008
Earlier work this paper cites.
Convergence results for some temporal difference methods based on least squares
Huizhen Yu and Dimitri P Bertsekas · 2009
Earlier work this paper cites.
Dynamic programming and optimal control 3rd edition, volume ii
Dimitri P Bertsekas · 2011
Earlier work this paper cites.
Temporal difference methods for general projected equations
Dimitri P Bertsekas · 2011
Earlier work this paper cites.
Maxout networks
Ian Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron Courville, and Yoshua Bengio · 2013
Earlier work this paper cites.
Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Earlier work this paper cites.
Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2015
Earlier work this paper cites.
Deep reinforcement learning for dialogue generation
Jiwei Li, Will Monroe, Alan Ritter, Michel Galley, Jianfeng Gao, and Dan Jurafsky · 2016
Cited alongside, same era.
Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates
Shixiang Gu, Ethan Holly, Timothy Lillicrap, and Sergey Levine · 2017
Cited alongside, same era.
Mastering chess and shogi by self-play with a general reinforcement learning algorithm
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, et al · 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, Adrian Bolton, et al · 2017
Cited alongside, same era.
A finite time analysis of temporal difference learning with linear function approximation
Neural temporal-difference learning converges to global optima
Qi Cai, Zhuoran Yang, Jason D Lee, and Zhaoran Wang · 2019
Later among the works it cites.
On lazy training in differentiable programming
Lenaic Chizat, Edouard Oyallon, and Francis Bach · 2019
Later among the works it cites.
Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow relu networks
Ziwei Ji and Matus Telgarsky · 2019
Later among the works it cites.
Neural tangent kernels, transportation mappings, and universal approximation
Ziwei Ji, Matus Telgarsky, and Ruicheng Xian · 2019
Later among the works it cites.
Overparameterized nonlinear learning: Gradient descent takes the shortest path?
Samet Oymak and Mahdi Soltanolkotabi · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jalaj Bhandari, Daniel Russo, and Raghav Singal · 2018
Cited alongside, same era.
Gradient descent provably optimizes over-parameterized neural networks
Simon S Du, Xiyu Zhai, Barnabas Poczos, and Aarti Singh · 2018
Cited alongside, same era.
Neural Tangent Kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
Cited alongside, same era.
Qt-opt: Scalable deep reinforcement learning for vision-based robotic manipulation
Dmitry Kalashnikov, Alex Irpan, Peter Pastor, Julian Ibarz, Alexander Herzog, Eric Jang, Deirdre Quillen, Ethan Holly, Mrinal Kalakrishnan, Vincent Vanhoucke, et al · 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.
Temporal-difference learning for nonlinear value function approximation in the lazy training regime
Andrea Agazzi and Jianfeng Lu · 2019
Cited alongside, same era.
Learning and generalization in overparameterized neural networks, going beyond two layers
Zeyuan Allen-Zhu, Yuanzhi Li, and Yingyu Liang · 2019
Cited alongside, same era.
Fine-grained analysis of optimization and generalization for overparameterized two-layer neural networks
Sanjeev Arora, Simon Du, Wei Hu, Zhiyuan Li, and Ruosong Wang · 2019
Cited alongside, same era.
Justin Sirignano and Konstantinos Spiliopoulos · 2019
Later among the works it cites.
Finite-time error bounds for linear stochastic approximation andtd learning
R. Srikant and Lei Ying · 2019
Later among the works it cites.
Neural policy gradient methods: Global optimality and rates of convergence
Lingxiao Wang, Qi Cai, Zhuoran Yang, and Zhaoran Wang · 2019
Later among the works it cites.
Geometric insights into the convergence of non-linear td learning
David Brandfonbrener and Joan Bruna · 2020
Later among the works it cites.
Toward moderate overparameterization: Global convergence guarantees for training shallow neural networks
Samet Oymak and Mahdi Soltanolkotabi · 2020
Later among the works it cites.
The role of regularization in overparameterized neural networks
Siddhartha Satpathi, Harsh Gupta, Shiyu Liang, and R Srikant · 2020
Later among the works it cites.
A finite-time analysis of Q-learning with neural network function approximation
Pan Xu and Quanquan Gu · 2020
Later among the works it cites.
Deep learning theory lecture notes
Matus Telgarsky · 2021
Closest in time.