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The Statistical Benefits of Quantile Temporal-Difference Learning for
  Value Estimation

The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation

28 May 2023
Mark Rowland
Yunhao Tang
Clare Lyle
Rémi Munos
Marc G. Bellemare
Will Dabney
ArXiv (abs)PDFHTML

Papers citing "The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation"

9 / 9 papers shown
Title
Demystifying the Paradox of Importance Sampling with an Estimated History-Dependent Behavior Policy in Off-Policy Evaluation
Demystifying the Paradox of Importance Sampling with an Estimated History-Dependent Behavior Policy in Off-Policy Evaluation
Hongyi Zhou
Josiah P. Hanna
Jin Zhu
Ying Yang
Chengchun Shi
OffRL
64
0
0
28 May 2025
Stop Regressing: Training Value Functions via Classification for
  Scalable Deep RL
Stop Regressing: Training Value Functions via Classification for Scalable Deep RL
Jesse Farebrother
Jordi Orbay
Q. Vuong
Adrien Ali Taïga
Yevgen Chebotar
...
Sergey Levine
Pablo Samuel Castro
Aleksandra Faust
Aviral Kumar
Rishabh Agarwal
OffRL
105
66
0
06 Mar 2024
More Benefits of Being Distributional: Second-Order Bounds for
  Reinforcement Learning
More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning
Kaiwen Wang
Owen Oertell
Alekh Agarwal
Nathan Kallus
Wen Sun
OffRL
125
12
0
11 Feb 2024
Beyond Average Return in Markov Decision Processes
Beyond Average Return in Markov Decision Processes
Alexandre Marthe
Aurélien Garivier
Claire Vernade
71
7
0
31 Oct 2023
Robust Offline Reinforcement learning with Heavy-Tailed Rewards
Robust Offline Reinforcement learning with Heavy-Tailed Rewards
Jin Zhu
Runzhe Wan
Zhengling Qi
Shuang Luo
C. Shi
OffRL
76
1
0
28 Oct 2023
Variance Control for Distributional Reinforcement Learning
Variance Control for Distributional Reinforcement Learning
Qi Kuang
Zhoufan Zhu
Liwen Zhang
Fan Zhou
OffRL
143
3
0
30 Jul 2023
An Analysis of Quantile Temporal-Difference Learning
An Analysis of Quantile Temporal-Difference Learning
Mark Rowland
Rémi Munos
M. G. Azar
Yunhao Tang
Georg Ostrovski
Anna Harutyunyan
K. Tuyls
Marc G. Bellemare
Will Dabney
159
24
0
11 Jan 2023
Distributional Reinforcement Learning by Sinkhorn Divergence
Distributional Reinforcement Learning by Sinkhorn Divergence
Ke Sun
Yingnan Zhao
Wulong Liu
Bei Jiang
Linglong Kong
76
0
0
01 Feb 2022
The Benefits of Being Categorical Distributional: Uncertainty-aware
  Regularized Exploration in Reinforcement Learning
The Benefits of Being Categorical Distributional: Uncertainty-aware Regularized Exploration in Reinforcement Learning
Ke Sun
Yingnan Zhao
Enze Shi
Yafei Wang
Xiaodong Yan
Bei Jiang
Linglong Kong
OODOffRLUQCV
86
2
0
07 Oct 2021
1