Saved in:
| Main Authors: | Zheng, Zewu, Lin, Yuanyuan |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.07831 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Policy Regularized Distributionally Robust Markov Decision Processes with Linear Function Approximation
by: Gu, Jingwen, et al.
Published: (2025)
by: Gu, Jingwen, et al.
Published: (2025)
Taming the Curses of Multiagency in Robust Markov Games with Large State Space through Linear Function Approximation
by: Gai, Jingchu, et al.
Published: (2026)
by: Gai, Jingchu, et al.
Published: (2026)
Refined Sample Complexity for Markov Games with Independent Linear Function Approximation
by: Dai, Yan, et al.
Published: (2024)
by: Dai, Yan, et al.
Published: (2024)
Convergence of Distributionally Robust Q-Learning with Linear Function Approximation
by: Mandal, Saptarshi, et al.
Published: (2025)
by: Mandal, Saptarshi, et al.
Published: (2025)
Sample-Efficient Tabular Self-Play for Offline Robust Reinforcement Learning
by: Li, Na, et al.
Published: (2025)
by: Li, Na, et al.
Published: (2025)
Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation
by: Liu, Zhishuai, et al.
Published: (2024)
by: Liu, Zhishuai, et al.
Published: (2024)
Linear Mixture Distributionally Robust Markov Decision Processes
by: Liu, Zhishuai, et al.
Published: (2025)
by: Liu, Zhishuai, et al.
Published: (2025)
Online Robust Reinforcement Learning with General Function Approximation
by: Ghosh, Debamita, et al.
Published: (2025)
by: Ghosh, Debamita, et al.
Published: (2025)
Accelerated Distributional Temporal Difference Learning with Linear Function Approximation
by: Jin, Kaicheng, et al.
Published: (2025)
by: Jin, Kaicheng, et al.
Published: (2025)
Sample Complexity of Offline Distributionally Robust Linear Markov Decision Processes
by: Wang, He, et al.
Published: (2024)
by: Wang, He, et al.
Published: (2024)
Online Semi-infinite Linear Programming: Efficient Algorithms via Function Approximation
by: Zong, Yiming, et al.
Published: (2026)
by: Zong, Yiming, et al.
Published: (2026)
RL in Markov Games with Independent Function Approximation: Improved Sample Complexity Bound under the Local Access Model
by: Fan, Junyi, et al.
Published: (2024)
by: Fan, Junyi, et al.
Published: (2024)
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)
Is Online Linear Optimization Sufficient for Strategic Robustness?
by: Cai, Yang, et al.
Published: (2026)
by: Cai, Yang, et al.
Published: (2026)
Misspecified $Q$-Learning with Sparse Linear Function Approximation: Tight Bounds on Approximation Error
by: Du, Ally Yalei, et al.
Published: (2024)
by: Du, Ally Yalei, et al.
Published: (2024)
Reinforcement Learning with Function Approximation for Non-Markov Processes
by: Kara, Ali Devran
Published: (2026)
by: Kara, Ali Devran
Published: (2026)
Doubly Outlier-Robust Online Infinite Hidden Markov Model
by: Yiu, Horace, et al.
Published: (2026)
by: Yiu, Horace, et al.
Published: (2026)
Follow The Approximate Sparse Leader for No-Regret Online Sparse Linear Approximation
by: Mukhopadhyay, Samrat, et al.
Published: (2025)
by: Mukhopadhyay, Samrat, et al.
Published: (2025)
Gap-Dependent Bounds for Nearly Minimax Optimal Reinforcement Learning with Linear Function Approximation
by: Zhang, Haochen, et al.
Published: (2026)
by: Zhang, Haochen, et al.
Published: (2026)
Replicable Reinforcement Learning with Linear Function Approximation
by: Eaton, Eric, et al.
Published: (2025)
by: Eaton, Eric, et al.
Published: (2025)
Nonstationary Reinforcement Learning with Linear Function Approximation
by: Zhou, Huozhi, et al.
Published: (2020)
by: Zhou, Huozhi, et al.
Published: (2020)
Rethinking the Global Convergence of Softmax Policy Gradient with Linear Function Approximation
by: Lin, Max Qiushi, et al.
Published: (2025)
by: Lin, Max Qiushi, et al.
Published: (2025)
Roping in Uncertainty: Robustness and Regularization in Markov Games
by: McMahan, Jeremy, et al.
Published: (2024)
by: McMahan, Jeremy, et al.
Published: (2024)
Strategically Robust Multi-Agent Reinforcement Learning with Linear Function Approximation
by: Gonzales, Jake, et al.
Published: (2026)
by: Gonzales, Jake, et al.
Published: (2026)
Thompson Sampling in Online RLHF with General Function Approximation
by: Feng, Songtao, et al.
Published: (2025)
by: Feng, Songtao, et al.
Published: (2025)
Reinforcement Learning with Function Approximation: From Linear to Nonlinear
by: Long, Jihao, et al.
Published: (2023)
by: Long, Jihao, et al.
Published: (2023)
An Actor-Critic Algorithm with Function Approximation for Risk Sensitive Cost Markov Decision Processes
by: Guin, Soumyajit, et al.
Published: (2025)
by: Guin, Soumyajit, et al.
Published: (2025)
Online Reinforcement Learning in Markov Decision Process Using Linear Programming
by: Leon, Vincent, et al.
Published: (2023)
by: Leon, Vincent, et al.
Published: (2023)
Online and Offline Robust Multivariate Linear Regression
by: Godichon-Baggioni, Antoine, et al.
Published: (2024)
by: Godichon-Baggioni, Antoine, et al.
Published: (2024)
Is Pure Exploitation Sufficient in Exogenous MDPs with Linear Function Approximation?
by: Liang, Hao, et al.
Published: (2026)
by: Liang, Hao, et al.
Published: (2026)
Statistical Inference for Temporal Difference Learning with Linear Function Approximation
by: Wu, Weichen, et al.
Published: (2024)
by: Wu, Weichen, et al.
Published: (2024)
Corruption-Robust Offline Two-Player Zero-Sum Markov Games
by: Nika, Andi, et al.
Published: (2024)
by: Nika, Andi, et al.
Published: (2024)
Regret and Sample Complexity of Online Q-Learning via Concentration of Stochastic Approximation with Time-Inhomogeneous Markov Chains
by: Singh, Rahul, et al.
Published: (2026)
by: Singh, Rahul, et al.
Published: (2026)
Functional Linear Regression of Cumulative Distribution Functions
by: Zhang, Qian, et al.
Published: (2022)
by: Zhang, Qian, et al.
Published: (2022)
Linear Convergence of Entropy-Regularized Natural Policy Gradient with Linear Function Approximation
by: Cayci, Semih, et al.
Published: (2021)
by: Cayci, Semih, et al.
Published: (2021)
Wasserstein Distributionally Robust Online Learning
by: Chen, Guixian, et al.
Published: (2026)
by: Chen, Guixian, et al.
Published: (2026)
Graph Neural Network-Based Distributed Optimal Control for Linear Networked Systems: An Online Distributed Training Approach
by: Song, Zihao, et al.
Published: (2025)
by: Song, Zihao, et al.
Published: (2025)
Provably Efficient Offline-to-Online Value Adaptation with General Function Approximation
by: Li, Shangzhe, et al.
Published: (2026)
by: Li, Shangzhe, et al.
Published: (2026)
A Switching System Theory of Q-Learning with Linear Function Approximation
by: Lee, Donghwan, et al.
Published: (2026)
by: Lee, Donghwan, et al.
Published: (2026)
Incentivize without Bonus: Provably Efficient Model-based Online Multi-agent RL for Markov Games
by: Yang, Tong, et al.
Published: (2025)
by: Yang, Tong, et al.
Published: (2025)
Similar Items
-
Policy Regularized Distributionally Robust Markov Decision Processes with Linear Function Approximation
by: Gu, Jingwen, et al.
Published: (2025) -
Taming the Curses of Multiagency in Robust Markov Games with Large State Space through Linear Function Approximation
by: Gai, Jingchu, et al.
Published: (2026) -
Refined Sample Complexity for Markov Games with Independent Linear Function Approximation
by: Dai, Yan, et al.
Published: (2024) -
Convergence of Distributionally Robust Q-Learning with Linear Function Approximation
by: Mandal, Saptarshi, et al.
Published: (2025) -
Sample-Efficient Tabular Self-Play for Offline Robust Reinforcement Learning
by: Li, Na, et al.
Published: (2025)