Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning?

7 October 2019

Papers citing "Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning?"

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Title
Regret Bounds for Stochastic Shortest Path Problems with Linear Function Approximation Daniel Vial Advait Parulekar Sanjay Shakkottai R. Srikant 42 15 0 04 May 2021
Learning Good State and Action Representations via Tensor Decomposition Chengzhuo Ni Yaqi Duan M. Dahleh Anru R. Zhang Mengdi Wang 26 7 0 03 May 2021
Linear Systems can be Hard to Learn Anastasios Tsiamis George J. Pappas 14 40 0 02 Apr 2021
Cautiously Optimistic Policy Optimization and Exploration with Linear Function Approximation Andrea Zanette Ching-An Cheng Alekh Agarwal 32 52 0 24 Mar 2021
An Exponential Lower Bound for Linearly-Realizable MDPs with Constant Suboptimality Gap Yuanhao Wang Ruosong Wang Sham Kakade OffRL 39 43 0 23 Mar 2021
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism Paria Rashidinejad Banghua Zhu Cong Ma Jiantao Jiao Stuart J. Russell OffRL 30 277 0 22 Mar 2021
Bilinear Classes: A Structural Framework for Provable Generalization in RL S. Du Sham Kakade Jason D. Lee Shachar Lovett G. Mahajan Wen Sun Ruosong Wang OffRL 38 188 0 19 Mar 2021
On The Effect of Auxiliary Tasks on Representation Dynamics Clare Lyle Mark Rowland Georg Ostrovski Will Dabney 34 69 0 25 Feb 2021
Model-free Representation Learning and Exploration in Low-rank MDPs Aditya Modi Jinglin Chen A. Krishnamurthy Nan Jiang Alekh Agarwal OffRL 102 78 0 14 Feb 2021
On Query-efficient Planning in MDPs under Linear Realizability of the Optimal State-value Function Gellert Weisz P. Amortila Barnabás Janzer Yasin Abbasi-Yadkori Nan Jiang Csaba Szepesvári OffRL 14 20 0 03 Feb 2021
Fast Rates for the Regret of Offline Reinforcement Learning Yichun Hu Nathan Kallus Masatoshi Uehara OffRL 13 30 0 31 Jan 2021
Improved Variance-Aware Confidence Sets for Linear Bandits and Linear Mixture MDP Zihan Zhang Jiaqi Yang Xiangyang Ji S. Du 71 36 0 29 Jan 2021
Provably Efficient Reinforcement Learning with Linear Function Approximation Under Adaptivity Constraints Chi Jin Zhuoran Yang Zhaoran Wang OffRL 122 166 0 06 Jan 2021
A Provably Efficient Algorithm for Linear Markov Decision Process with Low Switching Cost Minbo Gao Tianle Xie S. Du Lin F. Yang 36 45 0 02 Jan 2021
When Is Generalizable Reinforcement Learning Tractable? Dhruv Malik Yuanzhi Li Pradeep Ravikumar OffRL 86 24 0 01 Jan 2021
Privacy-Constrained Policies via Mutual Information Regularized Policy Gradients Chris Cundy Rishi Desai Stefano Ermon OffRL 33 4 0 30 Dec 2020
Towards Continual Reinforcement Learning: A Review and Perspectives Khimya Khetarpal Matthew D Riemer Irina Rish Doina Precup CLL OffRL 51 311 0 25 Dec 2020
Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes Dongruo Zhou Quanquan Gu Csaba Szepesvári 19 203 0 15 Dec 2020
Exponential Lower Bounds for Batch Reinforcement Learning: Batch RL can be Exponentially Harder than Online RL Andrea Zanette OffRL 26 71 0 14 Dec 2020
Logarithmic Regret for Reinforcement Learning with Linear Function Approximation Jiafan He Dongruo Zhou Quanquan Gu 17 92 0 23 Nov 2020
On Function Approximation in Reinforcement Learning: Optimism in the Face of Large State Spaces Zhuoran Yang Chi Jin Zhaoran Wang Mengdi Wang Michael I. Jordan 39 18 0 09 Nov 2020
What are the Statistical Limits of Offline RL with Linear Function Approximation? Ruosong Wang Dean Phillips Foster Sham Kakade OffRL 11 158 0 22 Oct 2020
Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective Dylan J. Foster Alexander Rakhlin D. Simchi-Levi Yunzong Xu 27 75 0 07 Oct 2020
Exponential Lower Bounds for Planning in MDPs With Linearly-Realizable Optimal Action-Value Functions Gellert Weisz P. Amortila Csaba Szepesvári OffRL 19 80 0 03 Oct 2020
Is Reinforcement Learning More Difficult Than Bandits? A Near-optimal Algorithm Escaping the Curse of Horizon Zihan Zhang Xiangyang Ji S. Du OffRL 12 103 0 28 Sep 2020
Latent Representation Prediction Networks Hlynur Davíð Hlynsson Merlin Schuler Robin Schiewer Tobias Glasmachers Laurenz Wiskott 8 1 0 20 Sep 2020
Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration Andrea Zanette A. Lazaric Mykel J. Kochenderfer Emma Brunskill 22 64 0 18 Aug 2020
On the Sample Complexity of Reinforcement Learning with Policy Space Generalization Wenlong Mou Zheng Wen Xi Chen 14 10 0 17 Aug 2020
Efficient Planning in Large MDPs with Weak Linear Function Approximation R. Shariff Csaba Szepesvári 39 22 0 13 Jul 2020
Extracting Latent State Representations with Linear Dynamics from Rich Observations Abraham Frandsen Rong Ge 6 2 0 29 Jun 2020
Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping Dongruo Zhou Jiafan He Quanquan Gu 30 133 0 23 Jun 2020
Towards Tractable Optimism in Model-Based Reinforcement Learning Aldo Pacchiano Philip J. Ball Jack Parker-Holder K. Choromanski Stephen J. Roberts OffRL 8 12 0 21 Jun 2020
On Reward-Free Reinforcement Learning with Linear Function Approximation Ruosong Wang S. Du Lin F. Yang Ruslan Salakhutdinov OffRL 27 105 0 19 Jun 2020
FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs Alekh Agarwal Sham Kakade A. Krishnamurthy Wen Sun OffRL 41 223 0 18 Jun 2020
$Q$ -learning with Logarithmic Regret Kunhe Yang Lin F. Yang S. Du 43 59 0 16 Jun 2020
Is Long Horizon Reinforcement Learning More Difficult Than Short Horizon Reinforcement Learning? Ruosong Wang S. Du Lin F. Yang Sham Kakade OffRL 10 52 0 01 May 2020
D4RL: Datasets for Deep Data-Driven Reinforcement Learning Justin Fu Aviral Kumar Ofir Nachum George Tucker Sergey Levine GP OffRL 75 1,315 0 15 Apr 2020
DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction Aviral Kumar Abhishek Gupta Sergey Levine OffRL 16 100 0 16 Mar 2020
Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning Fei Feng Ruosong Wang W. Yin S. Du Lin F. Yang OffRL SSL 30 7 0 15 Mar 2020
Provably Efficient Safe Exploration via Primal-Dual Policy Optimization Dongsheng Ding Xiaohan Wei Zhuoran Yang Zhaoran Wang M. Jovanović 20 159 0 01 Mar 2020
Learning Near Optimal Policies with Low Inherent Bellman Error Andrea Zanette A. Lazaric Mykel Kochenderfer Emma Brunskill OffRL 17 221 0 29 Feb 2020
Agnostic Q-learning with Function Approximation in Deterministic Systems: Tight Bounds on Approximation Error and Sample Complexity S. Du J. Lee G. Mahajan Ruosong Wang 10 37 0 17 Feb 2020
Learning Zero-Sum Simultaneous-Move Markov Games Using Function Approximation and Correlated Equilibrium Qiaomin Xie Yudong Chen Zhaoran Wang Zhuoran Yang 39 124 0 17 Feb 2020
Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles Dylan J. Foster Alexander Rakhlin 6 205 0 12 Feb 2020
Efficient and Robust Algorithms for Adversarial Linear Contextual Bandits Gergely Neu Julia Olkhovskaya 16 44 0 01 Feb 2020
Provably Efficient Exploration in Policy Optimization Qi Cai Zhuoran Yang Chi Jin Zhaoran Wang 13 277 0 12 Dec 2019
Optimism in Reinforcement Learning with Generalized Linear Function Approximation Yining Wang Ruosong Wang S. Du A. Krishnamurthy 135 135 0 09 Dec 2019
Comments on the Du-Kakade-Wang-Yang Lower Bounds Benjamin Van Roy Shi Dong 6 38 0 18 Nov 2019
Learning with Good Feature Representations in Bandits and in RL with a Generative Model Tor Lattimore Csaba Szepesvári Gellert Weisz OffRL 14 168 0 18 Nov 2019
Frequentist Regret Bounds for Randomized Least-Squares Value Iteration Andrea Zanette David Brandfonbrener Emma Brunskill Matteo Pirotta A. Lazaric 17 126 0 01 Nov 2019