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Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound

International Conference on Machine Learning (ICML), 2019

24 May 2019

Papers citing "Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound"

26 / 226 papers shown

Title
Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension Ruosong Wang Ruslan Salakhutdinov Lin F. Yang 205 55 0 21 May 2020
Is Long Horizon Reinforcement Learning More Difficult Than Short Horizon Reinforcement Learning? Ruosong Wang S. Du Lin F. Yang Sham Kakade OffRL 301 52 0 01 May 2020
Kernel-Based Reinforcement Learning: A Finite-Time AnalysisInternational Conference on Machine Learning (ICML), 2020 O. D. Domingues Pierre Ménard Matteo Pirotta E. Kaufmann Michal Valko 144 21 0 12 Apr 2020
Generative Adversarial Imitation Learning with Neural Networks: Global Optimality and Convergence RateInternational Conference on Machine Learning (ICML), 2020 Yufeng Zhang Qi Cai Zhuoran Yang Zhaoran Wang 291 12 0 08 Mar 2020
Provably Efficient Safe Exploration via Primal-Dual Policy OptimizationInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2020 Dongsheng Ding Xiaohan Wei Zhuoran Yang Zhaoran Wang M. Jovanović 292 175 0 01 Mar 2020
Learning Near Optimal Policies with Low Inherent Bellman ErrorInternational Conference on Machine Learning (ICML), 2020 Andrea Zanette A. Lazaric Mykel Kochenderfer Emma Brunskill OffRL 307 233 0 29 Feb 2020
Minimax-Optimal Off-Policy Evaluation with Linear Function ApproximationInternational Conference on Machine Learning (ICML), 2020 Yaqi Duan Mengdi Wang OffRL 236 156 0 21 Feb 2020
Agnostic Q-learning with Function Approximation in Deterministic Systems: Tight Bounds on Approximation Error and Sample Complexity S. Du Jason D. Lee G. Mahajan Ruosong Wang 110 39 0 17 Feb 2020
Learning Zero-Sum Simultaneous-Move Markov Games Using Function Approximation and Correlated EquilibriumAnnual Conference Computational Learning Theory (COLT), 2020 Qiaomin Xie Yudong Chen Zhaoran Wang Zhuoran Yang 336 134 0 17 Feb 2020
Conservative Exploration in Reinforcement LearningInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2020 Evrard Garcelon Mohammad Ghavamzadeh A. Lazaric Matteo Pirotta 230 28 0 08 Feb 2020
Adaptive Approximate Policy Iteration Botao Hao N. Lazić Yasin Abbasi-Yadkori Pooria Joulani Csaba Szepesvári 319 14 0 08 Feb 2020
Can Agents Learn by Analogy? An Inferable Model for PAC Reinforcement Learning Yanchao Sun Furong Huang 183 4 0 21 Dec 2019
Provably Efficient Reinforcement Learning with Aggregated States Shi Dong Benjamin Van Roy Zhengyuan Zhou 166 33 0 13 Dec 2019
Provably Efficient Exploration in Policy OptimizationInternational Conference on Machine Learning (ICML), 2019 Qi Cai Zhuoran Yang Chi Jin Zhaoran Wang 244 294 0 12 Dec 2019
Optimism in Reinforcement Learning with Generalized Linear Function ApproximationInternational Conference on Learning Representations (ICLR), 2019 Yining Wang Ruosong Wang S. Du A. Krishnamurthy 252 144 0 09 Dec 2019
Corruption-robust exploration in episodic reinforcement learningAnnual Conference Computational Learning Theory (COLT), 2019 Thodoris Lykouris Max Simchowitz Aleksandrs Slivkins Wen Sun 211 110 0 20 Nov 2019
Neural Contextual Bandits with UCB-based Exploration Dongruo Zhou Lihong Li Quanquan Gu 346 16 0 11 Nov 2019
Frequentist Regret Bounds for Randomized Least-Squares Value IterationInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2019 Andrea Zanette David Brandfonbrener Emma Brunskill Matteo Pirotta A. Lazaric 500 140 0 01 Nov 2019
Continuous Control with Contexts, Provably S. Du Ruosong Wang Mengdi Wang Lin F. Yang OffRL 108 5 0 30 Oct 2019
Sample Complexity of Reinforcement Learning using Linearly Combined Model EnsemblesInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2019 Aditya Modi Nan Jiang Ambuj Tewari Satinder Singh 224 138 0 23 Oct 2019
Adaptive Discretization for Episodic Reinforcement Learning in Metric SpacesProceedings of the ACM on Measurement and Analysis of Computing Systems (POMACS), 2019 Sean R. Sinclair Siddhartha Banerjee Chao Yu OffRL 160 41 0 17 Oct 2019
Uncertainty Quantification and Exploration for Reinforcement Learning Yi Zhu Jing Dong Henry Lam OffRL 350 7 0 12 Oct 2019
Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning?International Conference on Learning Representations (ICLR), 2019 S. Du Sham Kakade Ruosong Wang Lin F. Yang 384 207 0 07 Oct 2019
$$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank$ $\sqrt{n}$ -Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman RankAnnual Conference Computational Learning Theory (COLT), 2019 Kefan Dong Jian-wei Peng Yining Wang Yuanshuo Zhou OffRL 231 36 0 05 Sep 2019
Provably Efficient Reinforcement Learning with Linear Function ApproximationAnnual Conference Computational Learning Theory (COLT), 2019 Chi Jin Zhuoran Yang Zhaoran Wang Michael I. Jordan 289 616 0 11 Jul 2019
AsyncQVI: Asynchronous-Parallel Q-Value Iteration for Discounted Markov Decision Processes with Near-Optimal Sample Complexity Yibo Zeng Fei Feng W. Yin 176 3 0 03 Dec 2018