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1907.05388
Cited By
Provably Efficient Reinforcement Learning with Linear Function Approximation
11 July 2019
Chi Jin
Zhuoran Yang
Zhaoran Wang
Michael I. Jordan
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Papers citing
"Provably Efficient Reinforcement Learning with Linear Function Approximation"
50 / 151 papers shown
Title
Towards Optimal Differentially Private Regret Bounds in Linear MDPs
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Contextual Similarity Distillation: Ensemble Uncertainties with a Single Model
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Pascal R. van der Vaart
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0
14 Mar 2025
Stochastic Semi-Gradient Descent for Learning Mean Field Games with Population-Aware Function Approximation
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Xu Chen
Xuan Di
87
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17 Feb 2025
Incentivize without Bonus: Provably Efficient Model-based Online Multi-agent RL for Markov Games
Tong Yang
Bo Dai
Lin Xiao
Yuejie Chi
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64
2
0
13 Feb 2025
Provably Efficient Reinforcement Learning with Multinomial Logit Function Approximation
Long-Fei Li
Yu-Jie Zhang
Peng Zhao
Zhi-Hua Zhou
101
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0
17 Jan 2025
Mean-Field Sampling for Cooperative Multi-Agent Reinforcement Learning
Emile Anand
Ishani Karmarkar
Guannan Qu
83
1
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01 Dec 2024
RL-STaR: Theoretical Analysis of Reinforcement Learning Frameworks for Self-Taught Reasoner
Fu-Chieh Chang
Yu-Ting Lee
Hui-Ying Shih
Pei-Yuan Wu
Pei-Yuan Wu
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LRM
189
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0
31 Oct 2024
Efficient Model-Based Reinforcement Learning Through Optimistic Thompson Sampling
Jasmine Bayrooti
Carl Henrik Ek
Amanda Prorok
42
0
0
07 Oct 2024
Bellman Unbiasedness: Toward Provably Efficient Distributional Reinforcement Learning with General Value Function Approximation
Taehyun Cho
Seung Han
Kyungjae Lee
Seokhun Ju
Dohyeong Kim
Jungwoo Lee
66
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0
31 Jul 2024
Random Latent Exploration for Deep Reinforcement Learning
Srinath Mahankali
Zhang-Wei Hong
Ayush Sekhari
Alexander Rakhlin
Pulkit Agrawal
33
3
0
18 Jul 2024
BECAUSE: Bilinear Causal Representation for Generalizable Offline Model-based Reinforcement Learning
Hao-ming Lin
Wenhao Ding
Jian Chen
Laixi Shi
Jiacheng Zhu
Bo-wen Li
Ding Zhao
OffRL
CML
54
0
0
15 Jul 2024
Spectral Representation for Causal Estimation with Hidden Confounders
Tongzheng Ren
Haotian Sun
Antoine Moulin
Arthur Gretton
Bo Dai
CML
32
1
0
15 Jul 2024
Combinatorial Multivariant Multi-Armed Bandits with Applications to Episodic Reinforcement Learning and Beyond
Xutong Liu
Siwei Wang
Jinhang Zuo
Han Zhong
Xuchuang Wang
Zhiyong Wang
Shuai Li
Mohammad Hajiesmaili
J. C. Lui
Wei Chen
85
1
0
03 Jun 2024
Near-Optimal Regret in Linear MDPs with Aggregate Bandit Feedback
Asaf B. Cassel
Haipeng Luo
Aviv A. Rosenberg
Dmitry Sotnikov
OffRL
31
3
0
13 May 2024
Imitation Learning in Discounted Linear MDPs without exploration assumptions
Luca Viano
Stratis Skoulakis
V. Cevher
30
3
0
03 May 2024
Skill Transfer and Discovery for Sim-to-Real Learning: A Representation-Based Viewpoint
Haitong Ma
Zhaolin Ren
Bo Dai
Na Li
40
1
0
07 Apr 2024
Exploration is Harder than Prediction: Cryptographically Separating Reinforcement Learning from Supervised Learning
Noah Golowich
Ankur Moitra
Dhruv Rohatgi
OffRL
35
4
0
04 Apr 2024
Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithm
Miao Lu
Han Zhong
Tong Zhang
Jose H. Blanchet
OffRL
OOD
79
6
0
04 Apr 2024
Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation
Yu Chen
Xiangcheng Zhang
Siwei Wang
Longbo Huang
42
3
0
28 Feb 2024
No-Regret Reinforcement Learning in Smooth MDPs
Davide Maran
Alberto Maria Metelli
Matteo Papini
Marcello Restell
36
5
0
06 Feb 2024
On Sample-Efficient Offline Reinforcement Learning: Data Diversity, Posterior Sampling, and Beyond
Thanh Nguyen-Tang
Raman Arora
OffRL
33
3
0
06 Jan 2024
The Effective Horizon Explains Deep RL Performance in Stochastic Environments
Cassidy Laidlaw
Banghua Zhu
Stuart J. Russell
Anca Dragan
36
2
0
13 Dec 2023
Provable Representation with Efficient Planning for Partial Observable Reinforcement Learning
Hongming Zhang
Tongzheng Ren
Chenjun Xiao
Dale Schuurmans
Bo Dai
45
3
0
20 Nov 2023
Uncertainty-aware transfer across tasks using hybrid model-based successor feature reinforcement learning
Parvin Malekzadeh
Ming Hou
Konstantinos N. Plataniotis
51
1
0
16 Oct 2023
Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning
Yong Lin
Chen Liu
Chen Ye
Qing Lian
Yuan Yao
Tong Zhang
27
4
0
05 Sep 2023
Regret Analysis of Policy Gradient Algorithm for Infinite Horizon Average Reward Markov Decision Processes
Qinbo Bai
Washim Uddin Mondal
Vaneet Aggarwal
34
9
0
05 Sep 2023
Learning Optimal Admission Control in Partially Observable Queueing Networks
Jonatha Anselmi
B. Gaujal
Louis-Sébastien Rebuffi
26
1
0
04 Aug 2023
Stability of Q-Learning Through Design and Optimism
Sean P. Meyn
25
10
0
05 Jul 2023
Provably Robust Temporal Difference Learning for Heavy-Tailed Rewards
Semih Cayci
A. Eryilmaz
20
2
0
20 Jun 2023
The RL Perceptron: Generalisation Dynamics of Policy Learning in High Dimensions
Nishil Patel
Sebastian Lee
Stefano Sarao Mannelli
Sebastian Goldt
Adrew Saxe
OffRL
28
3
0
17 Jun 2023
Diverse Projection Ensembles for Distributional Reinforcement Learning
Moritz A. Zanger
Wendelin Bohmer
M. Spaan
30
4
0
12 Jun 2023
High-probability sample complexities for policy evaluation with linear function approximation
Gen Li
Weichen Wu
Yuejie Chi
Cong Ma
Alessandro Rinaldo
Yuting Wei
OffRL
30
6
0
30 May 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
28
20
0
29 May 2023
Reinforcement Learning with Human Feedback: Learning Dynamic Choices via Pessimism
Zihao Li
Zhuoran Yang
Mengdi Wang
OffRL
37
55
0
29 May 2023
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice
Toshinori Kitamura
Tadashi Kozuno
Yunhao Tang
Nino Vieillard
Michal Valko
...
Olivier Pietquin
M. Geist
Csaba Szepesvári
Wataru Kumagai
Yutaka Matsuo
OffRL
30
2
0
22 May 2023
Offline Primal-Dual Reinforcement Learning for Linear MDPs
Germano Gabbianelli
Gergely Neu
Nneka Okolo
Matteo Papini
OffRL
29
7
0
22 May 2023
Double Pessimism is Provably Efficient for Distributionally Robust Offline Reinforcement Learning: Generic Algorithm and Robust Partial Coverage
Jose H. Blanchet
Miao Lu
Tong Zhang
Han Zhong
OffRL
45
30
0
16 May 2023
A Theoretical Analysis of Optimistic Proximal Policy Optimization in Linear Markov Decision Processes
Han Zhong
Tong Zhang
35
26
0
15 May 2023
Stochastic Nonlinear Control via Finite-dimensional Spectral Dynamic Embedding
Tongzheng Ren
Zhaolin Ren
Haitong Ma
Na Li
Bo Dai
30
10
0
08 Apr 2023
Does Sparsity Help in Learning Misspecified Linear Bandits?
Jialin Dong
Lin F. Yang
25
1
0
29 Mar 2023
Improved Sample Complexity for Reward-free Reinforcement Learning under Low-rank MDPs
Yuan Cheng
Ruiquan Huang
J. Yang
Yitao Liang
OffRL
41
8
0
20 Mar 2023
Revisiting Weighted Strategy for Non-stationary Parametric Bandits
Jing Wang
Peng Zhao
Zhihong Zhou
35
5
0
05 Mar 2023
The Provable Benefits of Unsupervised Data Sharing for Offline Reinforcement Learning
Haotian Hu
Yiqin Yang
Qianchuan Zhao
Chongjie Zhang
OffRL
11
5
0
27 Feb 2023
VIPeR: Provably Efficient Algorithm for Offline RL with Neural Function Approximation
Thanh Nguyen-Tang
R. Arora
OffRL
46
5
0
24 Feb 2023
Provably Efficient Reinforcement Learning via Surprise Bound
Hanlin Zhu
Ruosong Wang
Jason D. Lee
OffRL
20
5
0
22 Feb 2023
Reinforcement Learning in a Birth and Death Process: Breaking the Dependence on the State Space
Jonatha Anselmi
B. Gaujal
Louis-Sébastien Rebuffi
27
2
0
21 Feb 2023
Reinforcement Learning with Function Approximation: From Linear to Nonlinear
Jihao Long
Jiequn Han
27
5
0
20 Feb 2023
Offline Learning in Markov Games with General Function Approximation
Yuheng Zhang
Yunru Bai
Nan Jiang
OffRL
21
8
0
06 Feb 2023
Learning in POMDPs is Sample-Efficient with Hindsight Observability
Jonathan Lee
Alekh Agarwal
Christoph Dann
Tong Zhang
34
19
0
31 Jan 2023
Improved Regret for Efficient Online Reinforcement Learning with Linear Function Approximation
Uri Sherman
Tomer Koren
Yishay Mansour
32
12
0
30 Jan 2023
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