Cover Image

Accelerated Distributional Temporal Difference Learning with Linear Function Approximation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Jin, Kaicheng, Peng, Yang, Yang, Jiansheng, Zhang, Zhihua
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A Finite Sample Analysis of Distributional TD Learning with Linear Function Approximation
by: Peng, Yang, et al.
Published: (2025)

Statistical Efficiency of Distributional Temporal Difference Learning and Freedman's Inequality in Hilbert Spaces
by: Peng, Yang, et al.
Published: (2024)

Asymptotic Time-Uniform Inference for Parameters in Averaged Stochastic Approximation
by: Xie, Chuhan, et al.
Published: (2024)

Statistical Inference for Temporal Difference Learning with Linear Function Approximation
by: Wu, Weichen, et al.
Published: (2024)

A Regularized Online Newton Method for Stochastic Convex Bandits with Linear Vanishing Noise
by: Zhan, Jingxin, et al.
Published: (2025)

Federated Temporal Difference Learning with Linear Function Approximation under Environmental Heterogeneity
by: Wang, Han, et al.
Published: (2023)

Estimation and Inference in Distributional Reinforcement Learning
by: Zhang, Liangyu, et al.
Published: (2023)

On the Generalization Properties of Learning the Random Feature Models with Learnable Activation Functions
by: Ma, Zailin, et al.
Published: (2025)

Gauss-Newton Temporal Difference Learning with Nonlinear Function Approximation
by: Ke, Zhifa, et al.
Published: (2023)

Misspecified $Q$-Learning with Sparse Linear Function Approximation: Tight Bounds on Approximation Error
by: Du, Ally Yalei, et al.
Published: (2024)

Convergence of Distributionally Robust Q-Learning with Linear Function Approximation
by: Mandal, Saptarshi, et al.
Published: (2025)

Learning Expressive Random Feature Models via Parametrized Activations
by: Ma, Zailin, et al.
Published: (2024)

Federated Control in Markov Decision Processes
by: Jin, Hao, et al.
Published: (2024)

Distributionally Robust Online Markov Game with Linear Function Approximation
by: Zheng, Zewu, et al.
Published: (2025)

Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation
by: Liu, Zhishuai, et al.
Published: (2024)

Replicable Reinforcement Learning with Linear Function Approximation
by: Eaton, Eric, et al.
Published: (2025)

Nonstationary Reinforcement Learning with Linear Function Approximation
by: Zhou, Huozhi, et al.
Published: (2020)

DRIK: Distribution-Robust Inductive Kriging without Information Leakage
by: Yang, Chen, et al.
Published: (2025)

Reinforcement Learning from Partial Observation: Linear Function Approximation with Provable Sample Efficiency
by: Cai, Qi, et al.
Published: (2022)

Reinforcement Learning with Function Approximation: From Linear to Nonlinear
by: Long, Jihao, et al.
Published: (2023)

Almost Sure Convergence of Linear Temporal Difference Learning with Arbitrary Features
by: Wang, Jiuqi, et al.
Published: (2024)

Hardware-Friendly Input Expansion for Accelerating Function Approximation
by: Lou, Hu, et al.
Published: (2026)

Accelerated Learning with Linear Temporal Logic using Differentiable Simulation
by: Bozkurt, Alper Kamil, et al.
Published: (2025)

Finite Sample Analysis of Linear Temporal Difference Learning with Arbitrary Features
by: Xie, Zixuan, et al.
Published: (2025)

Generalized Linear Markov Decision Process
by: Zhang, Sinian, et al.
Published: (2025)

Federated Reinforcement Learning with Constraint Heterogeneity
by: Jin, Hao, et al.
Published: (2024)

Corruption-Robust Offline Reinforcement Learning with General Function Approximation
by: Ye, Chenlu, et al.
Published: (2023)

Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation
by: Chen, Yu, et al.
Published: (2024)

Gap-Dependent Bounds for Nearly Minimax Optimal Reinforcement Learning with Linear Function Approximation
by: Zhang, Haochen, et al.
Published: (2026)

Stochastic Approximation Approaches to Group Distributionally Robust Optimization and Beyond
by: Zhang, Lijun, et al.
Published: (2023)

A Switching System Theory of Q-Learning with Linear Function Approximation
by: Lee, Donghwan, et al.
Published: (2026)

Decoupled Functional Central Limit Theorems for Two-Time-Scale Stochastic Approximation
by: Han, Yuze, et al.
Published: (2024)

Temporal Difference Learning with Constrained Initial Representations
by: Lyu, Jiafei, et al.
Published: (2026)

Policy Regularized Distributionally Robust Markov Decision Processes with Linear Function Approximation
by: Gu, Jingwen, et al.
Published: (2025)

Collision Probability Distribution Estimation via Temporal Difference Learning
by: Steinecker, Thomas, et al.
Published: (2024)

Provably and Practically Efficient Adversarial Imitation Learning with General Function Approximation
by: Xu, Tian, et al.
Published: (2024)

Provably Efficient Reinforcement Learning with Multinomial Logit Function Approximation
by: Li, Long-Fei, et al.
Published: (2024)

Functional Linear Regression of Cumulative Distribution Functions
by: Zhang, Qian, et al.
Published: (2022)

Temporal-Difference Learning Using Distributed Error Signals
by: Guan, Jonas, et al.
Published: (2024)

Personalized Multi-Agent Average Reward TD-Learning via Joint Linear Approximation
by: Wang, Leo Muxing, et al.
Published: (2026)