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Efficiently Solving MDPs with Stochastic Mirror Descent

Efficiently Solving MDPs with Stochastic Mirror Descent

28 August 2020
Yujia Jin
Aaron Sidford
ArXivPDFHTML

Papers citing "Efficiently Solving MDPs with Stochastic Mirror Descent"

12 / 12 papers shown
Title
Stochastic Halpern iteration in normed spaces and applications to reinforcement learning
Stochastic Halpern iteration in normed spaces and applications to reinforcement learning
Mario Bravo
Juan Pablo Contreras
76
4
0
19 Mar 2024
Coordinate Methods for Matrix Games
Coordinate Methods for Matrix Games
Y. Carmon
Yujia Jin
Aaron Sidford
Kevin Tian
35
33
0
17 Sep 2020
A Reduction from Reinforcement Learning to No-Regret Online Learning
A Reduction from Reinforcement Learning to No-Regret Online Learning
Ching-An Cheng
Rémi Tachet des Combes
Byron Boots
Geoffrey J. Gordon
OffRL
40
16
0
14 Nov 2019
Variance Reduction for Matrix Games
Variance Reduction for Matrix Games
Y. Carmon
Yujia Jin
Aaron Sidford
Kevin Tian
61
66
0
03 Jul 2019
Variance-reduced $Q$-learning is minimax optimal
Variance-reduced QQQ-learning is minimax optimal
Martin J. Wainwright
OffRL
59
92
0
11 Jun 2019
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
81
172
0
10 Jun 2019
Variance Reduced Value Iteration and Faster Algorithms for Solving
  Markov Decision Processes
Variance Reduced Value Iteration and Faster Algorithms for Solving Markov Decision Processes
Aaron Sidford
Mengdi Wang
X. Wu
Yinyu Ye
52
126
0
27 Oct 2017
Primal-Dual $π$ Learning: Sample Complexity and Sublinear Run Time for
  Ergodic Markov Decision Problems
Primal-Dual πππ Learning: Sample Complexity and Sublinear Run Time for Ergodic Markov Decision Problems
Mengdi Wang
147
70
0
17 Oct 2017
Learning Unknown Markov Decision Processes: A Thompson Sampling Approach
Learning Unknown Markov Decision Processes: A Thompson Sampling Approach
Ouyang Yi
Mukul Gagrani
A. Nayyar
R. Jain
49
128
0
14 Sep 2017
Stochastic Variance Reduction Methods for Saddle-Point Problems
Stochastic Variance Reduction Methods for Saddle-Point Problems
B. Palaniappan
Francis R. Bach
117
214
0
20 May 2016
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
Sublinear Optimization for Machine Learning
Sublinear Optimization for Machine Learning
K. Clarkson
Elad Hazan
David P. Woodruff
78
139
0
21 Oct 2010
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