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An $L^2$ Analysis of Reinforcement Learning in High Dimensions with Kernel and Neural Network Approximation

15 April 2021

Papers citing "An $L^2$ Analysis of Reinforcement Learning in High Dimensions with Kernel and Neural Network Approximation"

27 / 27 papers shown

Title
Towards a Mathematical Understanding of Neural Network-Based Machine Learning: what we know and what we don't E. Weinan Chao Ma Stephan Wojtowytsch Lei Wu AI4CE 88 134 0 22 Sep 2020
Deep Neural Tangent Kernel and Laplace Kernel Have the Same RKHS Lin Chen Sheng Xu 146 94 0 22 Sep 2020
On the Similarity between the Laplace and Neural Tangent Kernels Amnon Geifman A. Yadav Yoni Kasten Meirav Galun David Jacobs Ronen Basri 123 95 0 03 Jul 2020
Kolmogorov Width Decay and Poor Approximators in Machine Learning: Shallow Neural Networks, Random Feature Models and Neural Tangent Kernels E. Weinan Stephan Wojtowytsch 136 31 0 21 May 2020
Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension Ruosong Wang Ruslan Salakhutdinov Lin F. Yang 65 55 0 21 May 2020
Provably Efficient Exploration in Policy Optimization Qi Cai Zhuoran Yang Chi Jin Zhaoran Wang 66 283 0 12 Dec 2019
Optimism in Reinforcement Learning with Generalized Linear Function Approximation Yining Wang Ruosong Wang S. Du A. Krishnamurthy 182 137 0 09 Dec 2019
Frequentist Regret Bounds for Randomized Least-Squares Value Iteration Andrea Zanette David Brandfonbrener Emma Brunskill Matteo Pirotta A. Lazaric 76 132 0 01 Nov 2019
Neural Policy Gradient Methods: Global Optimality and Rates of Convergence Lingxiao Wang Qi Cai Zhuoran Yang Zhaoran Wang 96 242 0 29 Aug 2019
Provably Efficient Reinforcement Learning with Linear Function Approximation Chi Jin Zhuoran Yang Zhaoran Wang Michael I. Jordan 98 560 0 11 Jul 2019
The Barron Space and the Flow-induced Function Spaces for Neural Network Models E. Weinan Chao Ma Lei Wu 71 110 0 18 Jun 2019
On the Inductive Bias of Neural Tangent Kernels A. Bietti Julien Mairal 91 259 0 29 May 2019
Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound Lin F. Yang Mengdi Wang OffRL GP 64 288 0 24 May 2019
Information-Theoretic Considerations in Batch Reinforcement Learning Jinglin Chen Nan Jiang OOD OffRL 161 378 0 01 May 2019
A Comparative Analysis of the Optimization and Generalization Property of Two-layer Neural Network and Random Feature Models Under Gradient Descent Dynamics E. Weinan Chao Ma Lei Wu MLT 64 123 0 08 Apr 2019
Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks Sanjeev Arora S. Du Wei Hu Zhiyuan Li Ruosong Wang MLT 208 974 0 24 Jan 2019
A Theoretical Analysis of Deep Q-Learning Jianqing Fan Zhuoran Yang Yuchen Xie Zhaoran Wang 190 606 0 01 Jan 2019
A Priori Estimates of the Population Risk for Two-layer Neural Networks Weinan E Chao Ma Lei Wu 65 132 0 15 Oct 2018
Is Q-learning Provably Efficient? Chi Jin Zeyuan Allen-Zhu Sébastien Bubeck Michael I. Jordan OffRL 78 812 0 10 Jul 2018
Neural Tangent Kernel: Convergence and Generalization in Neural Networks Arthur Jacot Franck Gabriel Clément Hongler 273 3,219 0 20 Jun 2018
Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning Christoph Dann Tor Lattimore Emma Brunskill 78 311 0 22 Mar 2017
Minimax Regret Bounds for Reinforcement Learning M. G. Azar Ian Osband Rémi Munos 92 778 0 16 Mar 2017
Benchmarking Deep Reinforcement Learning for Continuous Control Yan Duan Xi Chen Rein Houthooft John Schulman Pieter Abbeel OffRL 94 1,695 0 22 Apr 2016
Breaking the Curse of Dimensionality with Convex Neural Networks Francis R. Bach 184 706 0 30 Dec 2014
Generalization and Exploration via Randomized Value Functions Ian Osband Benjamin Van Roy Zheng Wen 91 314 0 04 Feb 2014
Playing Atari with Deep Reinforcement Learning Volodymyr Mnih Koray Kavukcuoglu David Silver Alex Graves Ioannis Antonoglou Daan Wierstra Martin Riedmiller 129 12,265 0 19 Dec 2013
On the Sample Complexity of Reinforcement Learning with a Generative Model M. G. Azar Rémi Munos H. Kappen 76 156 0 27 Jun 2012

An L2L^2L2 Analysis of Reinforcement Learning in High Dimensions with Kernel and Neural Network Approximation

Papers citing "An $L^2$ Analysis of Reinforcement Learning in High Dimensions with Kernel and Neural Network Approximation"

An $L^2$ Analysis of Reinforcement Learning in High Dimensions with Kernel and Neural Network Approximation