Statistical Efficiency of Distributional Temporal Difference Learning and Freedman's Inequality in Hilbert Spaces
Fuente:
arXiv
Saved in:
| Main Authors: | Peng, Yang, Zhang, Liangyu, Zhang, Zhihua |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Accelerated Distributional Temporal Difference Learning with Linear Function Approximation
by: Jin, Kaicheng, et al.
Published: (2025)
by: Jin, Kaicheng, et al.
Published: (2025)
A Finite Sample Analysis of Distributional TD Learning with Linear Function Approximation
by: Peng, Yang, et al.
Published: (2025)
by: Peng, Yang, et al.
Published: (2025)
Estimation and Inference in Distributional Reinforcement Learning
by: Zhang, Liangyu, et al.
Published: (2023)
by: Zhang, Liangyu, et al.
Published: (2023)
Federated Reinforcement Learning with Constraint Heterogeneity
by: Jin, Hao, et al.
Published: (2024)
by: Jin, Hao, et al.
Published: (2024)
Federated Control in Markov Decision Processes
by: Jin, Hao, et al.
Published: (2024)
by: Jin, Hao, et al.
Published: (2024)
On the Statistical Benefits of Temporal Difference Learning
by: Cheikhi, David, et al.
Published: (2023)
by: Cheikhi, David, et al.
Published: (2023)
Online Regularized Statistical Learning in Reproducing Kernel Hilbert Space With Non-Stationary Data
by: Zhang, Xiwei, et al.
Published: (2024)
by: Zhang, Xiwei, et al.
Published: (2024)
An Empirical Bernstein Inequality for Dependent Data in Hilbert Spaces and Applications
by: Mirzaei, Erfan, et al.
Published: (2025)
by: Mirzaei, Erfan, et al.
Published: (2025)
Statistical Inference for Temporal Difference Learning with Linear Function Approximation
by: Wu, Weichen, et al.
Published: (2024)
by: Wu, Weichen, et al.
Published: (2024)
Stochastic Interpolants in Hilbert Spaces
by: Yu, James Boran, et al.
Published: (2026)
by: Yu, James Boran, et al.
Published: (2026)
Revisiting a Design Choice in Gradient Temporal Difference Learning
by: Qian, Xiaochi, et al.
Published: (2023)
by: Qian, Xiaochi, et al.
Published: (2023)
On the Divergence of Differential Temporal Difference Learning without Local Clocks
by: Antrobius, David, et al.
Published: (2026)
by: Antrobius, David, et al.
Published: (2026)
Collision Probability Distribution Estimation via Temporal Difference Learning
by: Steinecker, Thomas, et al.
Published: (2024)
by: Steinecker, Thomas, et al.
Published: (2024)
Flow Straight and Fast in Hilbert Space: Functional Rectified Flow
by: Zhang, Jianxin, et al.
Published: (2025)
by: Zhang, Jianxin, et al.
Published: (2025)
Transformers Can Learn Temporal Difference Methods for In-Context Reinforcement Learning
by: Wang, Jiuqi, et al.
Published: (2024)
by: Wang, Jiuqi, et al.
Published: (2024)
Temporal-Difference Learning Using Distributed Error Signals
by: Guan, Jonas, et al.
Published: (2024)
by: Guan, Jonas, et al.
Published: (2024)
Function Spaces Without Kernels: Learning Compact Hilbert Space Representations
by: Low, Su Ann, et al.
Published: (2025)
by: Low, Su Ann, et al.
Published: (2025)
Vector-Valued Distributional Reinforcement Learning Policy Evaluation: A Hilbert Space Embedding Approach
by: Mohammadi, Mehrdad, et al.
Published: (2026)
by: Mohammadi, Mehrdad, et al.
Published: (2026)
Temporal Difference Learning with Constrained Initial Representations
by: Lyu, Jiafei, et al.
Published: (2026)
by: Lyu, Jiafei, et al.
Published: (2026)
Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory
by: Zhang, Yufeng, et al.
Published: (2020)
by: Zhang, Yufeng, et al.
Published: (2020)
Distributional Random Forests for Complex Survey Designs on Reproducing Kernel Hilbert Spaces
by: Zou, Yating, et al.
Published: (2025)
by: Zou, Yating, et al.
Published: (2025)
Simplifying Deep Temporal Difference Learning
by: Gallici, Matteo, et al.
Published: (2024)
by: Gallici, Matteo, et al.
Published: (2024)
An Analysis of Quantile Temporal-Difference Learning
by: Rowland, Mark, et al.
Published: (2023)
by: Rowland, Mark, et al.
Published: (2023)
Gauss-Newton Temporal Difference Learning with Nonlinear Function Approximation
by: Ke, Zhifa, et al.
Published: (2023)
by: Ke, Zhifa, et al.
Published: (2023)
Learning Operators with Stochastic Gradient Descent in General Hilbert Spaces
by: Shi, Lei, et al.
Published: (2024)
by: Shi, Lei, et al.
Published: (2024)
Policy Newton Algorithm in Reproducing Kernel Hilbert Space
by: Zhang, Yixian, et al.
Published: (2025)
by: Zhang, Yixian, et al.
Published: (2025)
Almost Sure Convergence of Linear Temporal Difference Learning with Arbitrary Features
by: Wang, Jiuqi, et al.
Published: (2024)
by: Wang, Jiuqi, et al.
Published: (2024)
Statistical Inverse Problems in Hilbert Scales
by: Rastogi, Abhishake
Published: (2022)
by: Rastogi, Abhishake
Published: (2022)
A Finite-Iteration Theory for Asynchronous Categorical Distributional Temporal-Difference Learning
by: Kaya, Ege C., et al.
Published: (2026)
by: Kaya, Ege C., et al.
Published: (2026)
Function Encoders: A Principled Approach to Transfer Learning in Hilbert Spaces
by: Ingebrand, Tyler, et al.
Published: (2025)
by: Ingebrand, Tyler, et al.
Published: (2025)
Cochain Perspectives on Temporal-Difference Signals for Learning Beyond Markov Dynamics
by: Zhang, Zuyuan, et al.
Published: (2026)
by: Zhang, Zuyuan, et al.
Published: (2026)
Temporal Conformal Prediction (TCP): A Distribution-Free Statistical and Machine Learning Framework for Adaptive Risk Forecasting
by: Aich, Agnideep, et al.
Published: (2025)
by: Aich, Agnideep, et al.
Published: (2025)
Discerning Temporal Difference Learning
by: Ma, Jianfei
Published: (2023)
by: Ma, Jianfei
Published: (2023)
Backstepping Temporal Difference Learning
by: Lim, Han-Dong, et al.
Published: (2023)
by: Lim, Han-Dong, et al.
Published: (2023)
Towards Parameter-Free Temporal Difference Learning
by: Li, Yunxiang, et al.
Published: (2026)
by: Li, Yunxiang, et al.
Published: (2026)
Reinforcement Learning From State and Temporal Differences
by: Weaver, Lex, et al.
Published: (2025)
by: Weaver, Lex, et al.
Published: (2025)
New Versions of Gradient Temporal Difference Learning
by: Lee, Donghwan, et al.
Published: (2021)
by: Lee, Donghwan, et al.
Published: (2021)
A Variance Minimization Approach to Temporal-Difference Learning
by: Chen, Xingguo, et al.
Published: (2024)
by: Chen, Xingguo, et al.
Published: (2024)
Temporal Difference Learning for High-Dimensional PIDEs with Jumps
by: Lu, Liwei, et al.
Published: (2023)
by: Lu, Liwei, et al.
Published: (2023)
Finite Sample Analysis of Linear Temporal Difference Learning with Arbitrary Features
by: Xie, Zixuan, et al.
Published: (2025)
by: Xie, Zixuan, et al.
Published: (2025)
Similar Items
-
Accelerated Distributional Temporal Difference Learning with Linear Function Approximation
by: Jin, Kaicheng, et al.
Published: (2025) -
A Finite Sample Analysis of Distributional TD Learning with Linear Function Approximation
by: Peng, Yang, et al.
Published: (2025) -
Estimation and Inference in Distributional Reinforcement Learning
by: Zhang, Liangyu, et al.
Published: (2023) -
Federated Reinforcement Learning with Constraint Heterogeneity
by: Jin, Hao, et al.
Published: (2024) -
Federated Control in Markov Decision Processes
by: Jin, Hao, et al.
Published: (2024)