{"total":14,"rows":[{"id":"2209.08025","title":"Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizability","created":"2022-09-16","cited_by_count":36,"area":null},{"id":"2211.11030","title":"Adversarial Cheap Talk","created":"2022-11-20","cited_by_count":16,"area":null},{"id":"2206.02670","title":"Robust Adversarial Attacks Detection based on Explainable Deep Reinforcement Learning For UAV Guidance and Planning","created":"2022-06-06","cited_by_count":13,"area":null},{"id":"2207.10170","title":"Illusory Attacks: Information-Theoretic Detectability Matters in Adversarial Attacks","created":"2022-07-20","cited_by_count":12,"area":null},{"id":"2102.07244","title":"Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS","created":"2021-02-14","cited_by_count":10,"area":null},{"id":"2106.08746","title":"Real-time Adversarial Perturbations against Deep Reinforcement Learning Policies: Attacks and Defenses","created":"2021-06-16","cited_by_count":9,"area":null},{"id":"2104.10868","title":"Towards Adversarial Patch Analysis and Certified Defense against Crowd Counting","created":"2021-04-22","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":"2305.02605","title":"Toward Evaluating Robustness of Reinforcement Learning with Adversarial Policy","created":"2023-05-04","cited_by_count":3,"area":null},{"id":"2111.04865","title":"On Assessing The Safety of Reinforcement Learning algorithms Using Formal Methods","created":"2021-11-08","cited_by_count":2,"area":null},{"id":"2307.14751","title":"FLARE: Fingerprinting Deep Reinforcement Learning Agents using Universal Adversarial Masks","created":"2023-07-27","cited_by_count":2,"area":null},{"id":"2304.12151","title":"Policy Resilience to Environment Poisoning Attacks on Reinforcement Learning","created":"2023-04-24","cited_by_count":1,"area":null},{"id":"2205.07626","title":"Attacking and Defending Deep Reinforcement Learning Policies","created":"2022-05-16","cited_by_count":0,"area":null},{"id":"2508.15764","title":"Distributed Detection of Adversarial Attacks in Multi-Agent Reinforcement Learning with Continuous Action Space","created":"2025-08-21","cited_by_count":0,"area":null}],"next_cursor":null}