Safe RL

Papers

• Safe Continual Reinforcement Learning In Non-stationary Environments (2026)
  Austin Coursey, Abel Diaz-Gonzalez, Marcos Quinones-Grueiro, et al.
  12.89
• Safe And Accelerated Deep Reinforcement Learning-based O-RAN Slicing: A Hybrid Transfer Learning Approach (2023)
  Ahmad M. Nagib, Hatem Abou-Zeid, Hossam S. Hassanein
  11.29
• Context-aware Safe Reinforcement Learning For Non-stationary Environments (2021)
  Baiming Chen, Zuxin Liu, Jiacheng Zhu, et al.
  9.76
• Safe Reinforcement Learning With Dual Robustness (2023)
  Zeyang Li, Chuxiong Hu, Yunan Wang, et al.
  8.60
• A Safe Deep Reinforcement Learning Approach For Energy Efficient Federated Learning In Wireless Communication Networks (2023)
  Nikolaos Koursioumpas, Lina Magoula, Nikolaos Petropouleas, et al.
  8.09
• Smoothing Policies And Safe Policy Gradients (2019)
  Matteo Papini, Matteo Pirotta, Marcello Restelli
  7.50
• Physics-informed RL For Maximal Safety Probability Estimation (2024)
  Hikaru Hoshino, Yorie Nakahira
  5.24
• Model-based Safe Deep Reinforcement Learning Via A Constrained Proximal Policy Optimization Algorithm (2022)
  Ashish Kumar Jayant, Shalabh Bhatnagar
  5.24
• More For Less: Safe Policy Improvement With Stronger Performance Guarantees (2023)
  Patrick Wienhöft, Marnix Suilen, Thiago D. Simão, et al.
  5.24
• Reinforcement Learning By Guided Safe Exploration (2023)
  Qisong Yang, Thiago D. Simão, Nils Jansen, et al.
  5.24
• PREFINE: Preference-Based Implicit Reward and Cost Fine-Tuning for Safety Alignment (2026)
  Richa Verma et al.
  4.54
• Specialized Deep Residual Policy Safe Reinforcement Learning-based Controller For Complex And Continuous State-action Spaces (2023)
  Ammar N. Abbas, Georgios C. Chasparis, John D. Kelleher
  4.52
• Diverse Exploration For Fast And Safe Policy Improvement (2018)
  Andrew Cohen, Lei Yu, Robert Wright
  4.52
• Guided Online Distillation: Promoting Safe Reinforcement Learning By Offline Demonstration (2023)
  Jinning Li, Xinyi Liu, Banghua Zhu, et al.
  4.52
• Reinforcement Learning With Ensemble Model Predictive Safety Certification (2024)
  Sven Gronauer, Tom Haider, Felippe Schmoeller da Roza, et al.
  3.58
• Safety Correction From Baseline: Towards The Risk-aware Policy In Robotics Via Dual-agent Reinforcement Learning (2022)
  Linrui Zhang, Zichen Yan, Li Shen, et al.
  3.58
• Safe Reinforcement Learning In Black-box Environments Via Adaptive Shielding (2024)
  Daniel Bethell, Simos Gerasimou, Radu Calinescu, et al.
  2.26
• Meta Sac-lag: Towards Deployable Safe Reinforcement Learning Via Metagradient-based Hyperparameter Tuning (2024)
  Homayoun Honari, Amir Mehdi Soufi Enayati, Mehran Ghafarian Tamizi, et al.
  2.26
• Safe Reinforcement Learning For Constrained Markov Decision Processes With Stochastic Stopping Time (2024)
  Abhijit Mazumdar, Rafal Wisniewski, Manuela L. Bujorianu
  2.26
• Utility-based Reinforcement Learning: Unifying Single-objective And Multi-objective Reinforcement Learning (2024)
  Peter Vamplew, Cameron Foale, Conor F. Hayes, et al.
  2.26
• MaMa: A Game-Theoretic Approach for Designing Safe Agentic Systems (2026)
  Jonathan N\"other et al.
  0.00
• Hierarchical Framework For Interpretable And Probabilistic Model-based Safe Reinforcement Learning (2023)
  Ammar N. Abbas, Georgios C. Chasparis, John D. Kelleher
  0.00
• Actsafe: Active Exploration With Safety Constraints For Reinforcement Learning (2024)
  Yarden As, Bhavya Sukhija, Lenart Treven, et al.
  0.00
• Safe Exploration Using Bayesian World Models And Log-barrier Optimization (2024)
  Yarden As, Bhavya Sukhija, Andreas Krause
  0.00
• Safe Multiagent Coordination Via Entropic Exploration (2024)
  Ayhan Alp Aydeniz, Enrico Marchesini, Robert Loftin, et al.
  0.00
• Model-free Reinforcement Learning For Model-based Control: Towards Safe, Interpretable And Sample-efficient Agents (2025)
  Thomas Banker, Ali Mesbah
  0.00
• On The Global Optimality Of Policy Gradient Methods In General Utility Reinforcement Learning (2024)
  Anas Barakat, Souradip Chakraborty, Peihong Yu, et al.
  0.00
• Safe Imitation Learning Via Fast Bayesian Reward Inference From Preferences (2020)
  Daniel S. Brown, Russell Coleman, Ravi Srinivasan, et al.
  0.00
• DOPE: Doubly Optimistic And Pessimistic Exploration For Safe Reinforcement Learning (2021)
  Archana Bura, Aria Hasanzadezonuzy, Dileep Kalathil, et al.
  0.00
• Safemil: Learning Offline Safe Imitation Policy From Non-preferred Trajectories (2025)
  Returaj Burnwal, Nirav Pravinbhai Bhatt, Balaraman Ravindran
  0.00
• Constraint-adaptive Policy Switching For Offline Safe Reinforcement Learning (2024)
  Yassine Chemingui, Aryan Deshwal, Honghao Wei, et al.
  0.00
• Efficient Policy Evaluation With Safety Constraint For Reinforcement Learning (2024)
  Claire Chen, Shuze Daniel Liu, Shangtong Zhang
  0.00
• Towards Fast Safe Online Reinforcement Learning Via Policy Finetuning (2024)
  Keru Chen, Honghao Wei, Zhigang Deng, et al.
  0.00
• Decoupled Guidance Diffusion For Adaptive Offline Safe Reinforcement Learning (2026)
  Rufeng Chen, Zhaofan Zhang, Zhejiang Yang, et al.
  0.00
• Safe Reinforcement Learning In Tensor Reproducing Kernel Hilbert Space (2023)
  Xiaoyuan Cheng, Boli Chen, Liz Varga, et al.
  0.00
• How Does The Lagrangian Guide Safe Reinforcement Learning Through Diffusion Models? (2026)
  Xiaoyuan Cheng, Wenxuan Yuan, Boyang Li, et al.
  0.00
• Towards Safe Learning-based Non-linear Model Predictive Control Through Recurrent Neural Network Modeling (2026)
  Mihaela-Larisa Clement, Mónika Farsang, Agnes Poks, et al.
  0.00
• Safe Multi-agent Learning Via Trapping Regions (2023)
  Aleksander Czechowski, Frans A. Oliehoek
  0.00
• Deep SPI: Safe Policy Improvement Via World Models (2025)
  Florent Delgrange, Raphael Avalos, Willem Röpke
  0.00
• Provably Efficient Generalized Lagrangian Policy Optimization For Safe Multi-agent Reinforcement Learning (2023)
  Dongsheng Ding, Xiaohan Wei, Zhuoran Yang, et al.
  0.00
• Golden Handcuffs Make Safer AI Agents (2026)
  Aram Ebtekar, Michael K. Cohen
  0.00
• Dyna-style Safety Augmented Reinforcement Learning: Staying Safe In The Face Of Uncertainty (2026)
  Artur Eisele, Bernd Frauenknecht, Friedrich Solowjow, et al.
  0.00
• Leave No Trace: Learning To Reset For Safe And Autonomous Reinforcement Learning (2017)
  Benjamin Eysenbach, Shixiang Gu, Julian Ibarz, et al.
  0.00
• Discovering Agentic Safety Specifications From 1-bit Danger Signals (2026)
  Victor Gallego
  0.00
• Controlling Underestimation Bias In Constrained Reinforcement Learning For Safe Exploration (2026)
  Shiqing Gao, Jiaxin Ding, Luoyi Fu, et al.
  0.00
• Offline Safe Reinforcement Learning Using Trajectory Classification (2024)
  Ze Gong, Akshat Kumar, Pradeep Varakantham
  0.00
• Safe Reinforcement Learning Via Projection On A Safe Set: How To Achieve Optimality? (2020)
  Sebastien Gros, Mario Zanon, Alberto Bemporad
  0.00
• Co-activation Graph Analysis Of Safety-verified And Explainable Deep Reinforcement Learning Policies (2025)
  Dennis Gross, Helge Spieker
  0.00
• Criticality And Safety Margins For Reinforcement Learning (2024)
  Alexander Grushin, Walt Woods, Alvaro Velasquez, et al.
  0.00
• Enhancing Efficiency Of Safe Reinforcement Learning Via Sample Manipulation (2024)
  Shangding Gu, Laixi Shi, Yuhao Ding, et al.
  0.00