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Beyond No Regret: Instance-Dependent PAC Reinforcement Learning
v1v2 (latest)

Beyond No Regret: Instance-Dependent PAC Reinforcement Learning

5 August 2021
Andrew Wagenmaker
Max Simchowitz
Kevin Jamieson
ArXiv (abs)PDFHTML

Papers citing "Beyond No Regret: Instance-Dependent PAC Reinforcement Learning"

16 / 16 papers shown
Title
Gap-Dependent Bounds for Q-Learning using Reference-Advantage Decomposition
Gap-Dependent Bounds for Q-Learning using Reference-Advantage Decomposition
Zhong Zheng
Haochen Zhang
Lingzhou Xue
OffRL
127
2
0
10 Oct 2024
Reinforcement Learning from Human Feedback without Reward Inference: Model-Free Algorithm and Instance-Dependent Analysis
Reinforcement Learning from Human Feedback without Reward Inference: Model-Free Algorithm and Instance-Dependent Analysis
Qining Zhang
Honghao Wei
Lei Ying
OffRL
112
2
0
11 Jun 2024
What Are the Odds? Improving the foundations of Statistical Model Checking
What Are the Odds? Improving the foundations of Statistical Model Checking
Tobias Meggendorfer
Maximilian Weininger
Patrick Wienhoft
104
4
0
08 Apr 2024
Beyond Value-Function Gaps: Improved Instance-Dependent Regret Bounds
  for Episodic Reinforcement Learning
Beyond Value-Function Gaps: Improved Instance-Dependent Regret Bounds for Episodic Reinforcement Learning
Christoph Dann
T. V. Marinov
M. Mohri
Julian Zimmert
OffRL
48
30
0
02 Jul 2021
Task-Optimal Exploration in Linear Dynamical Systems
Task-Optimal Exploration in Linear Dynamical Systems
Andrew Wagenmaker
Max Simchowitz
Kevin Jamieson
67
18
0
10 Feb 2021
Is Reinforcement Learning More Difficult Than Bandits? A Near-optimal
  Algorithm Escaping the Curse of Horizon
Is Reinforcement Learning More Difficult Than Bandits? A Near-optimal Algorithm Escaping the Curse of Horizon
Zihan Zhang
Xiangyang Ji
S. Du
OffRL
103
105
0
28 Sep 2020
Fast active learning for pure exploration in reinforcement learning
Fast active learning for pure exploration in reinforcement learning
Pierre Ménard
O. D. Domingues
Anders Jonsson
E. Kaufmann
Edouard Leurent
Michal Valko
45
97
0
27 Jul 2020
Planning in Markov Decision Processes with Gap-Dependent Sample
  Complexity
Planning in Markov Decision Processes with Gap-Dependent Sample Complexity
Anders Jonsson
E. Kaufmann
Pierre Ménard
O. D. Domingues
Edouard Leurent
Michal Valko
46
33
0
10 Jun 2020
Breaking the Sample Size Barrier in Model-Based Reinforcement Learning
  with a Generative Model
Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model
Gen Li
Yuting Wei
Yuejie Chi
Yuxin Chen
99
129
0
26 May 2020
Model-Based Reinforcement Learning with a Generative Model is Minimax
  Optimal
Model-Based Reinforcement Learning with a Generative Model is Minimax Optimal
Alekh Agarwal
Sham Kakade
Lin F. Yang
OffRL
89
172
0
10 Jun 2019
Non-Asymptotic Gap-Dependent Regret Bounds for Tabular MDPs
Non-Asymptotic Gap-Dependent Regret Bounds for Tabular MDPs
Max Simchowitz
Kevin Jamieson
63
145
0
09 May 2019
Tighter Problem-Dependent Regret Bounds in Reinforcement Learning
  without Domain Knowledge using Value Function Bounds
Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds
Andrea Zanette
Emma Brunskill
OffRL
104
276
0
01 Jan 2019
Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement
  Learning
Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning
Christoph Dann
Tor Lattimore
Emma Brunskill
74
309
0
22 Mar 2017
On the Complexity of Best Arm Identification in Multi-Armed Bandit
  Models
On the Complexity of Best Arm Identification in Multi-Armed Bandit Models
E. Kaufmann
Olivier Cappé
Aurélien Garivier
193
1,025
0
16 Jul 2014
On the Sample Complexity of Reinforcement Learning with a Generative
  Model
On the Sample Complexity of Reinforcement Learning with a Generative Model
M. G. Azar
Rémi Munos
H. Kappen
71
156
0
27 Jun 2012
Empirical Bernstein Bounds and Sample Variance Penalization
Empirical Bernstein Bounds and Sample Variance Penalization
Andreas Maurer
Massimiliano Pontil
395
545
0
21 Jul 2009
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