Key papers

• Explainable Deep Reinforcement Learning: State Of The Art And Challenges (2023)
  George A. Vouros
  15.80
• New Challenges In Reinforcement Learning: A Survey Of Security And Privacy (2022)
  Yunjiao Lei, Dayong Ye, Sheng Shen, et al.
  10.85
• Adversary Agnostic Robust Deep Reinforcement Learning (2020)
  Xinghua Qu, Yew-Soon Ong, Abhishek Gupta, et al.
  6.77
• NormGuard: Reward-Preserving Norm Constraints in Flow-Matching Reinforcement Learning (2026)
  Tianlin Pan et al.
  5.82
• Large Language Models Hack Rewards, and Society (2026)
  Wei Liu et al.
  5.44
• Full Bayesian Reinforcement Learning via LF-IBIS (2026)
  Stefano Masini et al.
  5.01
• Predicting LLM Safety Before Release by Simulating Deployment (2026)
  Marcus Williams et al.
  5.01
• Institutional Red-Teaming: Deployment Rules, Not Just Models, Causally Shape Multi-Agent AI Safety (2026)
  Yujiao Chen
  5.01
• PERRY: Policy Evaluation with Confidence Intervals using Auxiliary Data (2025)
  Aishwarya Mandyam et al.
  4.93
• Learning Gait-Aware Quadruped Locomotion with Temporal Logic Specifications (2026)
  Merve Atasever et al.
  4.39
• The Rollout Infrastructure Tax in Coding-Agent Reinforcement Learning (2026)
  Daniel Thi Graviet et al.
  4.39
• Wind-Aware Reinforcement Learning Control of a Small Quadrotor Using Learned Onboard Wind Estimation in Simulated Atmospheric Turbulence (2026)
  Abdullah Al Tasim et al.
  4.39
• Can Reinforcement Learning Efficiently Discover Price Manipulation? (2026)
  Ioanna-Yvonni Tsaknaki et al.
  4.39
• When Certificates Fail: A Unified Safety Framework for Embedded Neural Interface Models (2026)
  Jasmeet Singh Bindra
  4.39
• Reasoning Consistency Scanning: A Framework for Auditing Chain-of-Thought Validity in AI Safety Evaluations (2026)
  Silvia Santano
  4.39
• Safe Reinforcement Learning using Ideas from Model Predictive Control (2026)
  Georg Sch\"afer et al.
  4.39
• RLVP: Penalize the Path, Reward the Outcome (2026)
  Bojie Li et al.
  4.39
• Avoiding unsafe sets when training with Langevin Dynamics (2026)
  Adam M. Oberman
  4.39
• Safety-Contract Graph Multi-Agent Reinforcement Learning for Autonomous Network Security Response (2026)
  Jose Luis Lima de Jesus Silva
  4.33
• Contract-Based Compositional Shielding for Safe Multi-Agent Reinforcement Learning (2026)
  Omar Adalat et al.
  4.33
• CSPO: Constraint-Sensitive Policy Optimization for Safe Reinforcement Learning (2026)
  Ayoub Belouadah et al.
  4.33
• Benchmarking Action Spaces in Reinforcement Learning for Vision-based Robotic Manipulation (2026)
  Seyed Alireza Azimi et al.
  4.33
• Learning to Trigger: Reinforcement Learning at the Large Hadron Collider (2026)
  Zixin Ding et al.
  4.33
• Themis: An explainable AI-enabled framework for Reinforcement Learning with Human Feedback (2026)
  Andreas Chouliaras et al.
  4.33
• Uncertainty-aware reinforcement learning for chemical language models (2026)
  Borja Medina et al.
  4.33
• Bias-Controlled Primal-Dual Natural Actor-Critic: Optimal Rates for Constrained Multi-Objective Average-Reward RL (2026)
  Ankur Naskar et al.
  4.33
• Power-Budgeted Underwater Vehicle Control via Constrained Reinforcement Learning (2026)
  Yinuo Wang et al.
  4.33
• FORCE: Efficient VLA Reinforcement Fine-Tuning via Value-Calibrated Warm-up and Self-Distillation (2026)
  Shuyi Zhang et al.
  4.33
• Regularized Reward-Punishment Reinforcement Learning (2026)
  Jiexin Wang et al.
  4.33
• Lightweight Safe Reinforcement Learning for End-to-End UAV Navigation (2026)
  Shenghui Zhang et al.
  3.51
• Provably Safe, Yet Scalable Reinforcement Learning (2026)
  Kai S. Yun et al.
  3.45
• Reinforcement Learning Towards Broadly and Persistently Beneficial Models (2026)
  Akshay V. Jagadeesh et al.
  3.45
• Reward-Conditioned Attention: How Reward Design Shapes What Autonomous Driving Agents See (2026)
  Mohamed Benabdelouahad et al.
  3.45
• Ensemble RL through Classifier Models: Enhancing Risk-Return Trade-offs in Trading Strategies (2025)
  Zheli Xiong
  2.65
• RAMAC: Multimodal Risk-Aware Offline Reinforcement Learning and the Role of Behavior Regularization (2025)
  Kai Fukazawa et al.
  2.56
• SLIM-RL: Risk-Budgeted Random-Masking RL for Diffusion LLMs Without Trajectory Slicing (2026)
  Ruikang Zhao et al.
  2.00
• Regret-aware Policy Optimization: Environment-level Memory For Replay Suppression Under Delayed Harm (2026)
  Prakul Sunil Hiremath
  1.83
• Bayesian Preference Learning for Test-Time Steerable Reward Models (2026)
  Jiwoo Hong et al.
  1.72
• TCRL: Temporal-Coupled Adversarial Training for Robust Constrained Reinforcement Learning in Worst-Case Scenarios (2026)
  Wentao Xu and Zhongming Yao and Weihao Li and Zhenghang Song and Yumeng Song and Tianyi Li and Yushuai Li
  1.72
• Safe Exploration via Policy Priors (2026)
  Manuel Wendl et al.
  1.67
• Learning Reward Functions for Cooperative Resilience in Multi-Agent Systems (2026)
  Manuela Chacon-Chamorro and Luis Felipe Giraldo and Nicanor Quijano
  1.67
• Convergence of Distributionally Robust Q-Learning with Linear Function Approximation (2025)
  Saptarshi Mandal et al.
  1.50
• Safety Assessment in Reinforcement Learning via Model Predictive Control (2025)
  Jeff Pflueger and Michael Everett
  1.50
• DuoGuard: A Two-Player RL-Driven Framework for Multilingual LLM Guardrails (2025)
  Yihe Deng et al.
  1.06
• CAT: Caution Aware Transfer In Reinforcement Learning Via Distributional Risk (2024)
  Mohamad Fares El Hajj Chehade, Amrit Singh Bedi, Amy Zhang, et al.
  0.00
• Provable Sim-to-real Transfer In Continuous Domain With Partial Observations (2022)
  Jiachen Hu, Han Zhong, Chi Jin, et al.
  0.00
• How Do You Act? An Empirical Study To Understand Behavior Of Deep Reinforcement Learning Agents (2020)
  Richard Meyes, Moritz Schneider, Tobias Meisen
  0.00
• FLARE: Fingerprinting Deep Reinforcement Learning Agents Using Universal Adversarial Masks (2023)
  Buse G. A. Tekgul, N. Asokan
  0.00
• Data-Efficient Reinforcement Learning in Continuous-State POMDPs (2016)
  Rowan McAllister et al.
  —
• Stochastic Shortest Path with Energy Constraints in POMDPs (2016)
  Tom\'a\v{s} Br\'azdil et al.
  —
• Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning (2016)
  Philip S. Thomas and Emma Brunskill
  —
• Avoiding Wireheading with Value Reinforcement Learning (2016)
  Tom Everitt et al.
  —
• Alternating Optimisation and Quadrature for Robust Control (2016)
  Supratik Paul et al.
  —
• Death and Suicide in Universal Artificial Intelligence (2016)
  Jarryd Martin et al.
  —
• Adapting Sampling Interval of Sensor Networks Using On-Line Reinforcement Learning (2016)
  Gabriel Martins Dias et al.
  —
• Safe and Efficient Off-Policy Reinforcement Learning (2016)
  R\'emi Munos et al.
  —
• Making Contextual Decisions with Low Technical Debt (2016)
  Alekh Agarwal et al.
  —
• Safe Exploration in Finite Markov Decision Processes with Gaussian Processes (2016)
  Matteo Turchetta et al.
  —
• On Reward Function for Survival (2016)
  Naoto Yoshida
  —
• Bootstrapping with Models: Confidence Intervals for Off-Policy Evaluation (2016)
  Josiah P. Hanna et al.
  —