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Nearly Minimax Optimal Reinforcement Learning with Linear Function Approximation
International Conference on Machine Learning (ICML), 2022
23 June 2022
Pihe Hu
Yu Chen
Longbo Huang
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Papers citing
"Nearly Minimax Optimal Reinforcement Learning with Linear Function Approximation"
27 / 27 papers shown
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Hybrid Reinforcement Learning Breaks Sample Size Barriers in Linear MDPs
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A Policy Gradient Primal-Dual Algorithm for Constrained MDPs with Uniform PAC Guarantees
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Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint
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Learning Adversarial Low-rank Markov Decision Processes with Unknown Transition and Full-information Feedback
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14 Nov 2023
A Doubly Robust Approach to Sparse Reinforcement Learning
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Pessimistic Nonlinear Least-Squares Value Iteration for Offline Reinforcement Learning
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Tackling Heavy-Tailed Rewards in Reinforcement Learning with Function Approximation: Minimax Optimal and Instance-Dependent Regret Bounds
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Han Zhong
Liwei Wang
Lin F. Yang
314
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Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice
International Conference on Machine Learning (ICML), 2023
Toshinori Kitamura
Tadashi Kozuno
Yunhao Tang
Nino Vieillard
Michal Valko
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Olivier Pietquin
Matthieu Geist
Csaba Szepesvári
Wataru Kumagai
Yutaka Matsuo
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179
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22 May 2023
A Theoretical Analysis of Optimistic Proximal Policy Optimization in Linear Markov Decision Processes
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Tong Zhang
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Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation
International Conference on Machine Learning (ICML), 2023
Yifei Min
Jiafan He
Tianhao Wang
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Variance-aware robust reinforcement learning with linear function approximation under heavy-tailed rewards
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237
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Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency
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Quanquan Gu
251
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21 Feb 2023
Improved Regret Bounds for Linear Adversarial MDPs via Linear Optimization
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Near-optimal Policy Identification in Active Reinforcement Learning
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Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes
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Near-Optimal Differentially Private Reinforcement Learning
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Dan Qiao
Yu Wang
330
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Near-Optimal Deployment Efficiency in Reward-Free Reinforcement Learning with Linear Function Approximation
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Yu Wang
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273
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0
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Understanding Deep Neural Function Approximation in Reinforcement Learning via
ε
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Fanghui Liu
Luca Viano
Volkan Cevher
313
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15 Sep 2022
Online Sub-Sampling for Reinforcement Learning with General Function Approximation
Dingwen Kong
Ruslan Salakhutdinov
Ruosong Wang
Lin F. Yang
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223
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Nonstationary Reinforcement Learning with Linear Function Approximation
Huozhi Zhou
Jinglin Chen
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