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Learning Near Optimal Policies with Low Inherent Bellman Error

29 February 2020

Mykel Kochenderfer

Papers citing "Learning Near Optimal Policies with Low Inherent Bellman Error"

50 / 94 papers shown

Title
Convergence of Policy Mirror Descent Beyond Compatible Function Approximation Uri Sherman Tomer Koren Yishay Mansour 52 0 0 16 Feb 2025
A Model Selection Approach for Corruption Robust Reinforcement Learning Chen-Yu Wei Christoph Dann Julian Zimmert 195 45 0 31 Dec 2024
On Sample-Efficient Offline Reinforcement Learning: Data Diversity, Posterior Sampling, and Beyond Thanh Nguyen-Tang Raman Arora OffRL 95 3 0 06 Jan 2024
Provable Benefits of Multi-task RL under Non-Markovian Decision Making Processes Ruiquan Huang Yuan Cheng Jing Yang Vincent Tan Yingbin Liang 75 0 0 20 Oct 2023
Uncertainty-aware transfer across tasks using hybrid model-based successor feature reinforcement learning Parvin Malekzadeh Ming Hou Konstantinos N. Plataniotis 92 1 0 16 Oct 2023
Kernelized Reinforcement Learning with Order Optimal Regret Bounds Sattar Vakili Julia Olkhovskaya 103 9 0 13 Jun 2023
Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo Haque Ishfaq Qingfeng Lan Pan Xu A. R. Mahmood Doina Precup Anima Anandkumar Kamyar Azizzadenesheli BDL OffRL 114 23 0 29 May 2023
Reinforcement Learning with Delayed, Composite, and Partially Anonymous Reward Washim Uddin Mondal Vaneet Aggarwal 83 2 0 04 May 2023
Provably Feedback-Efficient Reinforcement Learning via Active Reward Learning Dingwen Kong Lin F. Yang 99 12 0 18 Apr 2023
Does Sparsity Help in Learning Misspecified Linear Bandits? Jialin Dong Lin F. Yang 70 1 0 29 Mar 2023
On the Interplay Between Misspecification and Sub-optimality Gap in Linear Contextual Bandits Weitong Zhang Jiafan He Zhiyuan Fan Q. Gu 145 5 0 16 Mar 2023
Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency Heyang Zhao Jiafan He Dongruo Zhou Tong Zhang Quanquan Gu 101 28 0 21 Feb 2023
Reinforcement Learning with Function Approximation: From Linear to Nonlinear Jihao Long Jiequn Han 70 6 0 20 Feb 2023
Near-optimal Policy Identification in Active Reinforcement Learning Xiang Li Viraj Mehta Johannes Kirschner I. Char Willie Neiswanger J. Schneider Andreas Krause Ilija Bogunovic OffRL 85 6 0 19 Dec 2022
Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes Jiafan He Heyang Zhao Dongruo Zhou Quanquan Gu OffRL 136 55 0 12 Dec 2022
Model-Free Reinforcement Learning with the Decision-Estimation Coefficient Dylan J. Foster Noah Golowich Jian Qian Alexander Rakhlin Ayush Sekhari OffRL 94 10 0 25 Nov 2022
Leveraging Offline Data in Online Reinforcement Learning Andrew Wagenmaker Aldo Pacchiano OffRL OnRL 103 41 0 09 Nov 2022
Confident Approximate Policy Iteration for Efficient Local Planning in $q^π$ -realizable MDPs Gellert Weisz András Gyorgy Tadashi Kozuno Csaba Szepesvári 78 7 0 27 Oct 2022
Efficient Global Planning in Large MDPs via Stochastic Primal-Dual Optimization Gergely Neu Nneka Okolo 108 7 0 21 Oct 2022
A Reinforcement Learning Approach in Multi-Phase Second-Price Auction Design Rui Ai Boxiang Lyu Zhaoran Wang Zhuoran Yang Michael I. Jordan 78 3 0 19 Oct 2022
Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient Yuda Song Yi Zhou Ayush Sekhari J. Andrew Bagnell A. Krishnamurthy Wen Sun OffRL OnRL 94 105 0 13 Oct 2022
The Role of Coverage in Online Reinforcement Learning Tengyang Xie Dylan J. Foster Yu Bai Nan Jiang Sham Kakade OffRL 85 60 0 09 Oct 2022
Offline Reinforcement Learning with Differentiable Function Approximation is Provably Efficient Ming Yin Mengdi Wang Yu Wang OffRL 141 12 0 03 Oct 2022
Near-Optimal Deployment Efficiency in Reward-Free Reinforcement Learning with Linear Function Approximation Dan Qiao Yu Wang OffRL 123 13 0 03 Oct 2022
A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning Zixiang Chen C. J. Li An Yuan Quanquan Gu Michael I. Jordan OffRL 151 27 0 30 Sep 2022
Understanding Deep Neural Function Approximation in Reinforcement Learning via $ε$ -Greedy Exploration Fanghui Liu Luca Viano Volkan Cevher 116 19 0 15 Sep 2022
Future-Dependent Value-Based Off-Policy Evaluation in POMDPs Masatoshi Uehara Haruka Kiyohara Andrew Bennett Victor Chernozhukov Nan Jiang Nathan Kallus C. Shi Wen Sun OffRL 88 20 0 26 Jul 2022
Safe Exploration Incurs Nearly No Additional Sample Complexity for Reward-free RL Ruiquan Huang J. Yang Yingbin Liang OffRL 117 9 0 28 Jun 2022
Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems Masatoshi Uehara Ayush Sekhari Jason D. Lee Nathan Kallus Wen Sun OffRL 101 36 0 24 Jun 2022
Nearly Minimax Optimal Reinforcement Learning with Linear Function Approximation Pihe Hu Yu Chen Longbo Huang 86 35 0 23 Jun 2022
Sample-Efficient Reinforcement Learning of Partially Observable Markov Games Qinghua Liu Csaba Szepesvári Chi Jin 114 21 0 02 Jun 2022
Posterior Coreset Construction with Kernelized Stein Discrepancy for Model-Based Reinforcement Learning Souradip Chakraborty Amrit Singh Bedi Alec Koppel Brian M. Sadler Furong Huang Pratap Tokekar Tianyi Zhou 79 9 0 02 Jun 2022
Stabilizing Q-learning with Linear Architectures for Provably Efficient Learning Andrea Zanette Martin J. Wainwright OOD 125 5 0 01 Jun 2022
Provable General Function Class Representation Learning in Multitask Bandits and MDPs Rui Lu Andrew Zhao S. Du Gao Huang OffRL 104 10 0 31 May 2022
Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs Dongruo Zhou Quanquan Gu 122 45 0 23 May 2022
Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation Xiaoyu Chen Han Zhong Zhuoran Yang Zhaoran Wang Liwei Wang 192 70 0 23 May 2022
When Is Partially Observable Reinforcement Learning Not Scary? Qinghua Liu Alan Chung Csaba Szepesvári Chi Jin 80 98 0 19 Apr 2022
Jump-Start Reinforcement Learning Ikechukwu Uchendu Ted Xiao Yao Lu Banghua Zhu Mengyuan Yan ... Chuyuan Fu Cong Ma Jiantao Jiao Sergey Levine Karol Hausman OffRL OnRL 110 116 0 05 Apr 2022
Near-optimal Offline Reinforcement Learning with Linear Representation: Leveraging Variance Information with Pessimism Ming Yin Yaqi Duan Mengdi Wang Yu Wang OffRL 123 67 0 11 Mar 2022
A Complete Characterization of Linear Estimators for Offline Policy Evaluation Juan C. Perdomo A. Krishnamurthy Peter L. Bartlett Sham Kakade OffRL 76 3 0 08 Mar 2022
Computational-Statistical Gaps in Reinforcement Learning D. Kane Sihan Liu Shachar Lovett G. Mahajan 57 5 0 11 Feb 2022
Near-Optimal Regret for Adversarial MDP with Delayed Bandit Feedback Tiancheng Jin Tal Lancewicki Haipeng Luo Yishay Mansour Aviv A. Rosenberg 131 22 0 31 Jan 2022
Exponential Family Model-Based Reinforcement Learning via Score Matching Gen Li Junbo Li Anmol Kabra Nathan Srebro Zhaoran Wang Zhuoran Yang 90 4 0 28 Dec 2021
Nearly Optimal Policy Optimization with Stable at Any Time Guarantee Tianhao Wu Yunchang Yang Han Zhong Liwei Wang S. Du Jiantao Jiao 112 14 0 21 Dec 2021
Misspecified Gaussian Process Bandit Optimization Ilija Bogunovic Andreas Krause 95 45 0 09 Nov 2021
Improved Regret Analysis for Variance-Adaptive Linear Bandits and Horizon-Free Linear Mixture MDPs Yeoneung Kim Insoon Yang Kwang-Sung Jun 100 38 0 05 Nov 2021
Dealing With Misspecification In Fixed-Confidence Linear Top-m Identification Clémence Réda Andrea Tirinzoni Rémy Degenne 64 10 0 02 Nov 2021
Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection Matteo Papini Andrea Tirinzoni Aldo Pacchiano Marcello Restelli A. Lazaric Matteo Pirotta 86 20 0 27 Oct 2021
V-Learning -- A Simple, Efficient, Decentralized Algorithm for Multiagent RL Chi Jin Qinghua Liu Yuanhao Wang Tiancheng Yu OffRL 93 132 0 27 Oct 2021
Locally Differentially Private Reinforcement Learning for Linear Mixture Markov Decision Processes Chonghua Liao Jiafan He Quanquan Gu 77 17 0 19 Oct 2021