TCRL: Temporal-Coupled Adversarial Training for Robust Constrained Reinforcement Learning in Worst-Case Scenarios
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | Xu, Wentao, Yao, Zhongming, Li, Weihao, Song, Zhenghang, Song, Yumeng, Li, Tianyi, Li, Yushuai |
|---|---|
| Format: | Preprint |
| Publié: |
2026
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
FEAT: A Linear-Complexity Foundation Model for Extremely Large Structured Data
par: Song, Zhenghang, et autres
Publié: (2026)
par: Song, Zhenghang, et autres
Publié: (2026)
How Worst-Case Are Adversarial Attacks? Linking Adversarial and Perturbation Robustness
par: Rossolini, Giulio
Publié: (2026)
par: Rossolini, Giulio
Publié: (2026)
Adversarial Training for Robust Coverage Network under Worst-case Facility Losses
par: Miao, Changhao, et autres
Publié: (2026)
par: Miao, Changhao, et autres
Publié: (2026)
Infra-Bayesian Reinforcement Learning Agents Outperform Classical RL For Worst-Case Robustness
par: Aryal, Manish, et autres
Publié: (2026)
par: Aryal, Manish, et autres
Publié: (2026)
Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret
par: Zhong, Han, et autres
Publié: (2023)
par: Zhong, Han, et autres
Publié: (2023)
Robust off-policy Reinforcement Learning via Soft Constrained Adversary
par: Nakanishi, Kosuke, et autres
Publié: (2024)
par: Nakanishi, Kosuke, et autres
Publié: (2024)
Mildly Constrained Evaluation Policy for Offline Reinforcement Learning
par: Xu, Linjie, et autres
Publié: (2023)
par: Xu, Linjie, et autres
Publié: (2023)
Spurious Correlation-Aware Embedding Regularization for Worst-Group Robustness
par: Park, Subeen, et autres
Publié: (2025)
par: Park, Subeen, et autres
Publié: (2025)
Adversarial Training Can Provably Improve Robustness: Theoretical Analysis of Feature Learning Process Under Structured Data
par: Li, Binghui, et autres
Publié: (2024)
par: Li, Binghui, et autres
Publié: (2024)
Revisiting Bisimulation Metric for Robust Representations in Reinforcement Learning
par: Zhang, Leiji, et autres
Publié: (2025)
par: Zhang, Leiji, et autres
Publié: (2025)
MH-GIN: Multi-scale Heterogeneous Graph-based Imputation Network for AIS Data (Extended Version)
par: Liu, Hengyu, et autres
Publié: (2025)
par: Liu, Hengyu, et autres
Publié: (2025)
LoRaCompass: Robust Reinforcement Learning to Efficiently Search for a LoRa Tag
par: He, Tianlang, et autres
Publié: (2025)
par: He, Tianlang, et autres
Publié: (2025)
On the Clean Generalization and Robust Overfitting in Adversarial Training from Two Theoretical Views: Representation Complexity and Training Dynamics
par: Li, Binghui, et autres
Publié: (2023)
par: Li, Binghui, et autres
Publié: (2023)
CHGNN: A Semi-Supervised Contrastive Hypergraph Learning Network
par: Song, Yumeng, et autres
Publié: (2023)
par: Song, Yumeng, et autres
Publié: (2023)
Game-Theoretic Robust Reinforcement Learning Handles Temporally-Coupled Perturbations
par: Liang, Yongyuan, et autres
Publié: (2023)
par: Liang, Yongyuan, et autres
Publié: (2023)
Towards Optimal Adversarial Robust Reinforcement Learning with Infinity Measurement Error
par: Li, Haoran, et autres
Publié: (2025)
par: Li, Haoran, et autres
Publié: (2025)
Moon: A Modality Conversion-based Efficient Multivariate Time Series Anomaly Detection
par: Yao, Yuanyuan, et autres
Publié: (2025)
par: Yao, Yuanyuan, et autres
Publié: (2025)
Digital Twin-Empowered Voltage Control for Power Systems
par: Xu, Jiachen, et autres
Publié: (2024)
par: Xu, Jiachen, et autres
Publié: (2024)
Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment
par: Xian, Guanmeng, et autres
Publié: (2026)
par: Xian, Guanmeng, et autres
Publié: (2026)
Distribution Learning with Valid Outputs Beyond the Worst-Case
par: Rittler, Nick, et autres
Publié: (2024)
par: Rittler, Nick, et autres
Publié: (2024)
Learning from Peers: Collaborative Ensemble Adversarial Training
par: Dengjin, Li, et autres
Publié: (2025)
par: Dengjin, Li, et autres
Publié: (2025)
CRASH: Challenging Reinforcement-Learning Based Adversarial Scenarios For Safety Hardening
par: Kulkarni, Amar, et autres
Publié: (2024)
par: Kulkarni, Amar, et autres
Publié: (2024)
Robust Deep Reinforcement Learning Through Adversarial Attacks and Training : A Survey
par: Schott, Lucas, et autres
Publié: (2024)
par: Schott, Lucas, et autres
Publié: (2024)
Adversarial Diffusion for Robust Reinforcement Learning
par: Foffano, Daniele, et autres
Publié: (2025)
par: Foffano, Daniele, et autres
Publié: (2025)
Robust Driving Control for Autonomous Vehicles: An Intelligent General-sum Constrained Adversarial Reinforcement Learning Approach
par: Fan, Junchao, et autres
Publié: (2025)
par: Fan, Junchao, et autres
Publié: (2025)
Fourier Circuits in Neural Networks and Transformers: A Case Study of Modular Arithmetic with Multiple Inputs
par: Li, Chenyang, et autres
Publié: (2024)
par: Li, Chenyang, et autres
Publié: (2024)
Toward Evaluating Robustness of Reinforcement Learning with Adversarial Policy
par: Zheng, Xiang, et autres
Publié: (2023)
par: Zheng, Xiang, et autres
Publié: (2023)
RL-PINNs: Reinforcement Learning-Driven Adaptive Sampling for Efficient Training of PINNs
par: Song, Zhenao
Publié: (2025)
par: Song, Zhenao
Publié: (2025)
Injecting Distributional Awareness into MLLMs via Reinforcement Learning for Deep Imbalanced Regression
par: Du, Yao, et autres
Publié: (2026)
par: Du, Yao, et autres
Publié: (2026)
Temporal Difference Learning with Constrained Initial Representations
par: Lyu, Jiafei, et autres
Publié: (2026)
par: Lyu, Jiafei, et autres
Publié: (2026)
Better Representations via Adversarial Training in Pre-Training: A Theoretical Perspective
par: Xing, Yue, et autres
Publié: (2024)
par: Xing, Yue, et autres
Publié: (2024)
Adversarial Contrastive Learning for LLM Quantization Attacks
par: Song, Dinghong, et autres
Publié: (2026)
par: Song, Dinghong, et autres
Publié: (2026)
ConstrainedSQL: Training LLMs for Text2SQL via Constrained Reinforcement Learning
par: Chen, Weiqin, et autres
Publié: (2025)
par: Chen, Weiqin, et autres
Publié: (2025)
Prepared for the Worst: A Learning-Based Adversarial Attack for Resilience Analysis of the ICP Algorithm
par: Zhang, Ziyu, et autres
Publié: (2024)
par: Zhang, Ziyu, et autres
Publié: (2024)
Explainable Clustering Beyond Worst-Case Guarantees
par: Fleissner, Maximilian, et autres
Publié: (2024)
par: Fleissner, Maximilian, et autres
Publié: (2024)
Regularization for Adversarial Robust Learning
par: Wang, Jie, et autres
Publié: (2024)
par: Wang, Jie, et autres
Publié: (2024)
Predictive Lagrangian Optimization for Constrained Reinforcement Learning
par: Zhang, Tianqi, et autres
Publié: (2025)
par: Zhang, Tianqi, et autres
Publié: (2025)
Stop Training for the Worst: Progressive Unmasking Accelerates Masked Diffusion Training
par: Kim, Jaeyeon, et autres
Publié: (2026)
par: Kim, Jaeyeon, et autres
Publié: (2026)
HOSL: Hybrid-Order Split Learning for Memory-Constrained Edge Training
par: Lnu, Aakriti, et autres
Publié: (2026)
par: Lnu, Aakriti, et autres
Publié: (2026)
Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning
par: Surdej, Rafał, et autres
Publié: (2025)
par: Surdej, Rafał, et autres
Publié: (2025)
Documents similaires
-
FEAT: A Linear-Complexity Foundation Model for Extremely Large Structured Data
par: Song, Zhenghang, et autres
Publié: (2026) -
How Worst-Case Are Adversarial Attacks? Linking Adversarial and Perturbation Robustness
par: Rossolini, Giulio
Publié: (2026) -
Adversarial Training for Robust Coverage Network under Worst-case Facility Losses
par: Miao, Changhao, et autres
Publié: (2026) -
Infra-Bayesian Reinforcement Learning Agents Outperform Classical RL For Worst-Case Robustness
par: Aryal, Manish, et autres
Publié: (2026) -
Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret
par: Zhong, Han, et autres
Publié: (2023)