{"total":81,"rows":[{"id":"2101.08452","title":"Robust Reinforcement Learning on State Observations with Learned Optimal Adversary","created":"2021-01-21","cited_by_count":135,"area":null},{"id":"2108.00401","title":"Advances in adversarial attacks and defenses in computer vision: A survey","created":"2021-08-01","cited_by_count":127,"area":null},{"id":"2205.01992","title":"Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning","created":"2022-05-04","cited_by_count":94,"area":"safety"},{"id":"2106.05087","title":"Who Is the Strongest Enemy? Towards Optimal and Efficient Evasion Attacks in Deep RL","created":"2021-06-09","cited_by_count":55,"area":null},{"id":"2210.05927","title":"Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement Learning","created":"2022-10-12","cited_by_count":43,"area":null},{"id":"2312.12585","title":"BadRL: Sparse Targeted Backdoor Attack Against Reinforcement Learning","created":"2023-12-19","cited_by_count":30,"area":null},{"id":"2110.04471","title":"Provably Efficient Black-Box Action Poisoning Attacks Against Reinforcement Learning","created":"2021-10-09","cited_by_count":29,"area":null},{"id":"2102.08492","title":"Reward Poisoning in Reinforcement Learning: Attacks Against Unknown Learners in Unknown Environments","created":"2021-02-16","cited_by_count":26,"area":null},{"id":"2203.08398","title":"COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks","created":"2022-03-16","cited_by_count":26,"area":"offlinerl"},{"id":"2107.08828","title":"Reinforcement Learning for Education: Opportunities and Challenges","created":"2021-07-15","cited_by_count":25,"area":null},{"id":"2102.05776","title":"Defense Against Reward Poisoning Attacks in Reinforcement Learning","created":"2021-02-10","cited_by_count":21,"area":null},{"id":"2208.13663","title":"Understanding the Limits of Poisoning Attacks in Episodic Reinforcement Learning","created":"2022-08-29","cited_by_count":21,"area":null},{"id":"2206.01888","title":"Reward Poisoning Attacks on Offline Multi-Agent Reinforcement Learning","created":"2022-06-04","cited_by_count":20,"area":"offlinerl"},{"id":"2402.06734","title":"Corruption Robust Offline Reinforcement Learning with Human Feedback","created":"2024-02-09","cited_by_count":20,"area":"alignment"},{"id":"2107.00783","title":"Reinforcement Learning for Feedback-Enabled Cyber Resilience","created":"2021-07-02","cited_by_count":19,"area":null},{"id":"2405.20539","title":"SleeperNets: Universal Backdoor Poisoning Attacks Against Reinforcement Learning Agents","created":"2024-05-30","cited_by_count":19,"area":"safety"},{"id":"2302.03322","title":"Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority Influence","created":"2023-02-07","cited_by_count":17,"area":null},{"id":"2405.10531","title":"Nonparametric Teaching of Implicit Neural Representations","created":"2024-05-17","cited_by_count":15,"area":null},{"id":"2302.13851","title":"Implicit Poisoning Attacks in Two-Agent Reinforcement Learning: Adversarial Policies for Training-Time Attacks","created":"2023-02-27","cited_by_count":14,"area":null},{"id":"2401.00298","title":"Principal-Agent Reward Shaping in MDPs","created":"2023-12-30","cited_by_count":13,"area":null},{"id":"2201.02185","title":"Admissible Policy Teaching through Reward Design","created":"2022-01-06","cited_by_count":12,"area":null},{"id":"2106.00600","title":"Fair Clustering Using Antidote Data","created":"2021-06-01","cited_by_count":10,"area":null},{"id":"2110.09008","title":"When Are Linear Stochastic Bandits Attackable?","created":"2021-10-18","cited_by_count":10,"area":null},{"id":"2110.14432","title":"Iterative Teaching by Label Synthesis","created":"2021-10-27","cited_by_count":10,"area":null},{"id":"2307.07670","title":"Efficient Adversarial Attacks on Online Multi-agent Reinforcement Learning","created":"2023-07-15","cited_by_count":10,"area":null},{"id":"2205.14842","title":"Efficient Reward Poisoning Attacks on Online Deep Reinforcement Learning","created":"2022-05-30","cited_by_count":9,"area":null},{"id":"2306.03007","title":"Nonparametric Iterative Machine Teaching","created":"2023-06-05","cited_by_count":9,"area":null},{"id":"2110.11763","title":"Game Redesign in No-regret Game Playing","created":"2021-10-18","cited_by_count":8,"area":null},{"id":"2210.17467","title":"Iterative Teaching by Data Hallucination","created":"2022-10-31","cited_by_count":8,"area":null},{"id":"2309.01107","title":"Solving Non-Rectangular Reward-Robust MDPs via Frequency Regularization","created":"2023-09-03","cited_by_count":8,"area":null},{"id":"2402.12673","title":"Beyond Worst-case Attacks: Robust RL with Adaptive Defense via Non-dominated Policies","created":"2024-02-20","cited_by_count":8,"area":"deeprl"},{"id":"2201.00383","title":"Integrating Artificial Intelligence and Augmented Reality in Robotic Surgery: An Initial dVRK Study Using a Surgical Education Scenario","created":"2022-01-02","cited_by_count":7,"area":null},{"id":"2301.00188","title":"New Challenges in Reinforcement Learning: A Survey of Security and Privacy","created":"2022-12-31","cited_by_count":7,"area":null},{"id":"2311.10318","title":"Nonparametric Teaching for Multiple Learners","created":"2023-11-17","cited_by_count":7,"area":null},{"id":"2402.03590","title":"Assessing the Impact of Distribution Shift on Reinforcement Learning Performance","created":"2024-02-05","cited_by_count":7,"area":"deeprl"},{"id":"2303.02725","title":"Local Environment Poisoning Attacks on Federated Reinforcement Learning","created":"2023-03-05","cited_by_count":6,"area":null},{"id":"2212.05337","title":"Targeted Adversarial Attacks on Deep Reinforcement Learning Policies via Model Checking","created":"2022-12-10","cited_by_count":5,"area":null},{"id":"2305.10681","title":"Black-Box Targeted Reward Poisoning Attack Against Online Deep Reinforcement Learning","created":"2023-05-18","cited_by_count":5,"area":null},{"id":"2505.14170","title":"Nonparametric Teaching for Graph Property Learners","created":"2025-05-20","cited_by_count":5,"area":null},{"id":"2505.19532","title":"Fox in the Henhouse: Supply-Chain Backdoor Attacks Against Reinforcement Learning","created":"2025-05-26","cited_by_count":5,"area":"deeprl"},{"id":"2402.09695","title":"Universal Black-Box Reward Poisoning Attack against Offline Reinforcement Learning","created":"2024-02-15","cited_by_count":4,"area":"offlinerl"},{"id":"2403.07933","title":"Corruption-Robust Offline Two-Player Zero-Sum Markov Games","created":"2024-03-04","cited_by_count":4,"area":null},{"id":"2404.04714","title":"Data Poisoning Attacks on Off-Policy Policy Evaluation Methods","created":"2024-04-06","cited_by_count":4,"area":"safety"},{"id":"2407.15168","title":"Mitigating Deep Reinforcement Learning Backdoors in the Neural Activation Space","created":"2024-07-21","cited_by_count":4,"area":"deeprl"},{"id":"2112.05367","title":"Efficient Action Poisoning Attacks on Linear Contextual Bandits","created":"2021-12-10","cited_by_count":3,"area":null},{"id":"2303.12981","title":"Connected Superlevel Set in (Deep) Reinforcement Learning and its Application to Minimax Theorems","created":"2023-03-23","cited_by_count":3,"area":null},{"id":"2311.00582","title":"Minimally Modifying a Markov Game to Achieve Any Nash Equilibrium and Value","created":"2023-11-01","cited_by_count":3,"area":null},{"id":"2402.07019","title":"Informativeness of Reward Functions in Reinforcement Learning","created":"2024-02-10","cited_by_count":3,"area":"deeprl"},{"id":"2210.16915","title":"Imitating Opponent to Win: Adversarial Policy Imitation Learning in Two-player Competitive Games","created":"2022-10-30","cited_by_count":2,"area":"offlinerl"},{"id":"2212.06123","title":"Security of Deep Reinforcement Learning for Autonomous Driving: A Survey","created":"2022-12-12","cited_by_count":2,"area":"driving"},{"id":"2310.05308","title":"Adversarial Attacks on Combinatorial Multi-Armed Bandits","created":"2023-10-08","cited_by_count":2,"area":null},{"id":"2406.03862","title":"Robust Deep Reinforcement Learning against Adversarial Behavior Manipulation","created":"2024-06-06","cited_by_count":2,"area":"deeprl"},{"id":"2412.00797","title":"Online Poisoning Attack Against Reinforcement Learning under Black-box Environments","created":"2024-12-01","cited_by_count":2,"area":"deeprl"},{"id":"2503.10228","title":"Policy Teaching via Data Poisoning in Learning from Human Preferences","created":"2025-03-13","cited_by_count":2,"area":"alignment"},{"id":"2602.19844","title":"LLM-enabled Applications Require System-Level Threat Monitoring","created":"2026-02-23","cited_by_count":2,"area":null},{"id":"2606.18697","title":"Stealthy World Model Manipulation via Data Poisoning","created":"2026-06-17","cited_by_count":2,"area":"wm"},{"id":"2104.13302","title":"Adaptive Adversarial Training for Meta Reinforcement Learning","created":"2021-04-27","cited_by_count":1,"area":null},{"id":"2111.14833","title":"Adversarial Attacks in Cooperative AI","created":"2021-11-29","cited_by_count":1,"area":null},{"id":"2203.05774","title":"Reinforcement Learning for Linear Quadratic Control is Vulnerable Under Cost Manipulation","created":"2022-03-11","cited_by_count":1,"area":"offlinerl"},{"id":"2304.12151","title":"Policy Resilience to Environment Poisoning Attacks on Reinforcement Learning","created":"2023-04-24","cited_by_count":1,"area":null},{"id":"2306.08041","title":"Data Poisoning to Fake a Nash Equilibrium in Markov Games","created":"2023-06-13","cited_by_count":1,"area":"safety"},{"id":"2406.17114","title":"Inception: Efficiently Computable Misinformation Attacks on Markov Games","created":"2024-06-24","cited_by_count":1,"area":null},{"id":"2507.03372","title":"Action Robust Reinforcement Learning via Optimal Adversary Aware Policy Optimization","created":"2025-07-04","cited_by_count":1,"area":"deeprl"},{"id":"2507.04883","title":"Beyond Training-time Poisoning: Component-level and Post-training Backdoors in Deep Reinforcement Learning","created":"2025-07-07","cited_by_count":1,"area":"deeprl"},{"id":"2602.20461","title":"Nonparametric Teaching of Attention Learners","created":"2026-02-24","cited_by_count":1,"area":null},{"id":"2605.02495","title":"Efficient Preference Poisoning Attack on Offline RLHF","created":"2026-05-04","cited_by_count":1,"area":"alignment"},{"id":"2107.07038","title":"Conditional Teaching Size","created":"2021-06-29","cited_by_count":0,"area":null},{"id":"2311.15399","title":"Optimally Teaching a Linear Behavior Cloning Agent","created":"2023-11-26","cited_by_count":0,"area":null},{"id":"2401.02652","title":"Adaptive Discounting of Training Time Attacks","created":"2024-01-05","cited_by_count":0,"area":"safety"},{"id":"2401.05710","title":"The Distributional Reward Critic Framework for Reinforcement Learning Under Perturbed Rewards","created":"2024-01-11","cited_by_count":0,"area":"alignment"},{"id":"2405.06689","title":"Policy Iteration for Two-Player General-Sum Stochastic Stackelberg Games","created":"2024-05-07","cited_by_count":0,"area":null},{"id":"2503.03676","title":"Optimally Installing Strict Equilibria","created":"2025-03-05","cited_by_count":0,"area":null},{"id":"2506.06891","title":"Robust In-Context Reinforcement Learning Under Reward Poisoning Attacks","created":"2025-06-07","cited_by_count":0,"area":"deeprl"},{"id":"2506.16753","title":"Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation","created":"2025-06-20","cited_by_count":0,"area":"deeprl"},{"id":"2507.18113","title":"Policy Disruption in Reinforcement Learning:Adversarial Attack with Large Language Models and Critical State Identification","created":"2025-07-24","cited_by_count":0,"area":null},{"id":"2508.09275","title":"Constrained Black-Box Attacks Against Cooperative Multi-Agent Reinforcement Learning","created":"2025-08-12","cited_by_count":0,"area":null},{"id":"2510.01479","title":"Density-Ratio Weighted Behavioral Cloning: Learning Control Policies from Corrupted Datasets","created":"2025-10-01","cited_by_count":0,"area":"offlinerl"},{"id":"2510.12939","title":"Pruning Cannot Hurt Robustness: Certified Trade-offs in Reinforcement Learning","created":"2025-10-14","cited_by_count":0,"area":"deeprl"},{"id":"2510.13792","title":"Provably Invincible Adversarial Attacks on Reinforcement Learning Systems: A Rate-Distortion Information-Theoretic Approach","created":"2025-10-15","cited_by_count":0,"area":"deeprl"},{"id":"2606.00414","title":"Auditing Near-Optimal Policies Can Be Exponentially Hard: Conditional Query Lower Bounds via Occupancy Rashomon Capacity","created":"2026-05-29","cited_by_count":0,"area":null},{"id":"2606.09559","title":"Safe-RULE: Safe Reinforcement UnLEarning","created":"2026-06-08","cited_by_count":0,"area":"safety"}],"next_cursor":null}