Saved in:
| Main Authors: | Gai, Jingchu, Shi, Laixi |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.03125 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Breaking the Curse of Multiagency in Robust Multi-Agent Reinforcement Learning
by: Shi, Laixi, et al.
Published: (2024)
by: Shi, Laixi, et al.
Published: (2024)
Overcoming the Curse of Dimensionality in Reinforcement Learning Through Approximate Factorization
by: Lu, Chenbei, et al.
Published: (2024)
by: Lu, Chenbei, et al.
Published: (2024)
Sample Complexity of Offline Distributionally Robust Linear Markov Decision Processes
by: Wang, He, et al.
Published: (2024)
by: Wang, He, et al.
Published: (2024)
Tractable Equilibrium Computation in Markov Games through Risk Aversion
by: Mazumdar, Eric, et al.
Published: (2024)
by: Mazumdar, Eric, et al.
Published: (2024)
Distributionally Robust Online Markov Game with Linear Function Approximation
by: Zheng, Zewu, et al.
Published: (2025)
by: Zheng, Zewu, et al.
Published: (2025)
Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity
by: Shi, Laixi, et al.
Published: (2022)
by: Shi, Laixi, et al.
Published: (2022)
Differential Smoothing Mitigates Sharpening and Improves LLM Reasoning
by: Gai, Jingchu, et al.
Published: (2025)
by: Gai, Jingchu, et al.
Published: (2025)
Momentum Streams for Optimizer-Inspired Transformers
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)
Homomorphism Expressivity of Spectral Invariant Graph Neural Networks
by: Gai, Jingchu, et al.
Published: (2025)
by: Gai, Jingchu, et al.
Published: (2025)
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)
Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty
by: Shi, Laixi, et al.
Published: (2024)
by: Shi, Laixi, et al.
Published: (2024)
Lossless Anti-Distillation Sampling
by: Diao, Zibo, et al.
Published: (2026)
by: Diao, Zibo, et al.
Published: (2026)
T2S-MPC: Time-Embedded Online Adaptive Model Predictive Control for Time-Varying Dynamics
by: Shen, Zeyu, et al.
Published: (2026)
by: Shen, Zeyu, et al.
Published: (2026)
Conceptual Belief-Informed Reinforcement Learning
by: Gu, Xingrui, et al.
Published: (2024)
by: Gu, Xingrui, et al.
Published: (2024)
Distributionally Robust Constrained Reinforcement Learning under Strong Duality
by: Zhang, Zhengfei, et al.
Published: (2024)
by: Zhang, Zhengfei, et al.
Published: (2024)
SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning
by: Mangold, Paul, et al.
Published: (2024)
by: Mangold, Paul, et al.
Published: (2024)
Beyond Weisfeiler-Lehman: A Quantitative Framework for GNN Expressiveness
by: Zhang, Bohang, et al.
Published: (2024)
by: Zhang, Bohang, et al.
Published: (2024)
Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices
by: Woo, Jiin, et al.
Published: (2024)
by: Woo, Jiin, et al.
Published: (2024)
The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model
by: Shi, Laixi, et al.
Published: (2023)
by: Shi, Laixi, et al.
Published: (2023)
Tame Riemannian Stochastic Approximation
by: Aspman, Johannes, et al.
Published: (2023)
by: Aspman, Johannes, et al.
Published: (2023)
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)
Robust Gymnasium: A Unified Modular Benchmark for Robust Reinforcement Learning
by: Gu, Shangding, et al.
Published: (2025)
by: Gu, Shangding, et al.
Published: (2025)
Hybrid Transfer Reinforcement Learning: Provable Sample Efficiency from Shifted-Dynamics Data
by: Qu, Chengrui, et al.
Published: (2024)
by: Qu, Chengrui, 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)
The Curse of Depth in Large Language Models
by: Sun, Wenfang, et al.
Published: (2025)
by: Sun, Wenfang, et al.
Published: (2025)
Taming Momentum: Rethinking Optimizer States Through Low-Rank Approximation
by: Wang, Zhengbo, et al.
Published: (2026)
by: Wang, Zhengbo, et al.
Published: (2026)
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)
KL-regularization Itself is Differentially Private in Bandits and RLHF
by: Zhang, Yizhou, et al.
Published: (2025)
by: Zhang, Yizhou, et al.
Published: (2025)
Reinforcement Learning with Function Approximation for Non-Markov Processes
by: Kara, Ali Devran
Published: (2026)
by: Kara, Ali Devran
Published: (2026)
Nonstationary Reinforcement Learning with Linear Function Approximation
by: Zhou, Huozhi, et al.
Published: (2020)
by: Zhou, Huozhi, et al.
Published: (2020)
Replicable Reinforcement Learning with Linear Function Approximation
by: Eaton, Eric, et al.
Published: (2025)
by: Eaton, Eric, et al.
Published: (2025)
Learning Stable and Robust Linear Parameter-Varying State-Space Models
by: Verhoek, Chris, et al.
Published: (2023)
by: Verhoek, Chris, et al.
Published: (2023)
Linear Mixture Distributionally Robust Markov Decision Processes
by: Liu, Zhishuai, et al.
Published: (2025)
by: Liu, Zhishuai, 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)
PRIMER: Perception-Aware Robust Learning-based Multiagent Trajectory Planner
by: Kondo, Kota, et al.
Published: (2024)
by: Kondo, Kota, 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)
Reinforcement Learning with Function Approximation: From Linear to Nonlinear
by: Long, Jihao, et al.
Published: (2023)
by: Long, Jihao, et al.
Published: (2023)
StableSSM: Alleviating the Curse of Memory in State-space Models through Stable Reparameterization
by: Wang, Shida, et al.
Published: (2023)
by: Wang, Shida, et al.
Published: (2023)
Understanding the Curse of Unrolling
by: Mehmood, Sheheryar, et al.
Published: (2026)
by: Mehmood, Sheheryar, et al.
Published: (2026)
Similar Items
-
Breaking the Curse of Multiagency in Robust Multi-Agent Reinforcement Learning
by: Shi, Laixi, et al.
Published: (2024) -
Overcoming the Curse of Dimensionality in Reinforcement Learning Through Approximate Factorization
by: Lu, Chenbei, et al.
Published: (2024) -
Sample Complexity of Offline Distributionally Robust Linear Markov Decision Processes
by: Wang, He, et al.
Published: (2024) -
Tractable Equilibrium Computation in Markov Games through Risk Aversion
by: Mazumdar, Eric, et al.
Published: (2024) -
Distributionally Robust Online Markov Game with Linear Function Approximation
by: Zheng, Zewu, et al.
Published: (2025)