mlsanityData to 7 Oct 2026

Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning

arXiv 2001.09684 ↗First posted 27 Jan 2020cs.LG cs.AI cs.CRcited by 61

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Abstract (arXiv)

Deep Reinforcement Learning (DRL) has numerous applications in the real world thanks to its outstanding ability in quickly adapting to the surrounding environments. Despite its great advantages, DRL is susceptible to adversarial attacks, which precludes its use in real-life critical systems and applications (e.g., smart grids, traffic controls, and autonomous vehicles) unless its vulnerabilities are addressed and mitigated. Thus, this paper provides a comprehensive survey that discusses emerging attacks in DRL-based systems and the potential countermeasures to defend against these attacks. We first cover some fundamental backgrounds about DRL and present emerging adversarial attacks on machine learning techniques. We then investigate more details of the vulnerabilities that the adversary can exploit to attack DRL along with the state-of-the-art countermeasures to prevent such attacks. Finally, we highlight open issues and research challenges for developing solutions to deal with attacks for DRL-based intelligent systems.

Key facts. Cited by 61 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Jan 2020.

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Citing papers per year: 2021: 7; 2022: 16; 2023: 10; 2024: 10; 2025: 16; 2026: 2.

61 papers.

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  1. 2022I Know What You Trained Last Summer: A Survey on Stealing Machine Learning Models and Defencescited by 75
  2. 2022Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizabilitycited by 32
  3. 2022Adversarial Policies Beat Superhuman Go AIscited by 29
  4. 2022RoMFAC: A robust mean-field actor-critic reinforcement learning against adversarial perturbations on statescited by 16
  5. 2023Robust multi-agent coordination via evolutionary generation of auxiliary adversarial attackerscited by 15
  6. 2021Neural Network Verification in Controlcited by 14
  7. 2022Robust Adversarial Attacks Detection based on Explainable Deep Reinforcement Learning For UAV Guidance and Planningcited by 13
  8. 2022A Search-Based Testing Approach for Deep Reinforcement Learning Agentscited by 12
  9. 2022Illusory Attacks: Information-Theoretic Detectability Matters in Adversarial Attackscited by 12
  10. 2021Exploring the Training Robustness of Distributional Reinforcement Learning against Noisy State Observationscited by 11
  11. 2022Targeted Adversarial Attacks against Neural Network Trajectory Predictorscited by 11
  12. 2023ORL-AUDITOR: Dataset Auditing in Offline Deep Reinforcement Learningcited by 11
38 more
  1. 2024DDM-Lag : A Diffusion-based Decision-making Model for Autonomous Vehicles with Lagrangian Safety Enhancementcited by 11
  2. 2021Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPScited by 10
  3. 2023Can Agents Run Relay Race with Strangers? Generalization of RL to Out-of-Distribution Trajectoriescited by 9
  4. 2023Vision-based Learning for Drones: A Surveycited by 9
  5. 2023Robustness Testing for Multi-Agent Reinforcement Learning: State Perturbations on Critical Agentscited by 7
  6. 2025State-Aware Perturbation Optimization for Robust Deep Reinforcement Learningcited by 7
  7. 2025Robust Deep Reinforcement Learning in Robotics via Adaptive Gradient-Masked Adversarial Attackscited by 7
  8. 2021A Graph Policy Network Approach for Volt-Var Control in Power Distribution Systemscited by 5
  9. 2022Targeted Adversarial Attacks on Deep Reinforcement Learning Policies via Model Checkingcited by 5
  10. 2023Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and Challengescited by 5
  11. 2025Simulating the Unseen: Crash Prediction Must Learn from What Did Not Happencited by 5
  12. 2022Adversarial joint attacks on legged robotscited by 4
  13. 2024A Comprehensive Survey on the Security of Smart Grid: Challenges, Mitigations, and Future Research Opportunitiescited by 4
  14. 2022Emerging Threats in Deep Learning-Based Autonomous Driving: A Comprehensive Surveycited by 3
  15. 2023SoK: Adversarial Machine Learning Attacks and Defences in Multi-Agent Reinforcement Learningcited by 3
  16. 2024Data-Driven Lipschitz Continuity: A Cost-Effective Approach to Improve Adversarial Robustnesscited by 3
  17. 2024Benchmarking Deep Reinforcement Learning for Navigation in Denied Sensor Environmentscited by 3
  18. 2025Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approachcited by 3
  19. 2021Attacking Deep Reinforcement Learning-Based Traffic Signal Control Systems with Colluding Vehiclescited by 2
  20. 2021On Assessing The Safety of Reinforcement Learning algorithms Using Formal Methodscited by 2
  21. 2021Scope and Sense of Explainability for AI-Systemscited by 2
  22. 2022Adversarial Body Shape Search for Legged Robotscited by 2
  23. 2023A Reminder of its Brittleness: Language Reward Shaping May Hinder Learning for Instruction Following Agentscited by 2
  24. 2024Belief-Enriched Pessimistic Q-Learning against Adversarial State Perturbationscited by 2
  25. 2025Enhancing Security in Deep Reinforcement Learning: A Comprehensive Survey on Adversarial Attacks and Defensescited by 2
  26. 2022GFCL: A GRU-based Federated Continual Learning Framework against Data Poisoning Attacks in IoVcited by 1
  27. 2022A Transferable and Automatic Tuning of Deep Reinforcement Learning for Cost Effective Phishing Detectioncited by 1
  28. 2022Security of Deep Reinforcement Learning for Autonomous Driving: A Surveycited by 1
  29. 2023Mitigating Adversarial Attacks in Deepfake Detection: An Exploration of Perturbation and AI Techniquescited by 1
  30. 2024Private Knowledge Sharing in Distributed Learning: A Surveycited by 1
  31. 2024Provably Efficient Action-Manipulation Attack Against Continuous Reinforcement Learningcited by 1
  32. 2025Curriculum-Adapted Robust Reinforcement Learning for UAV Deconfliction in Adversarial Environmentscited by 1
  33. 2025ARMOR: Robust Reinforcement Learning-based Control for UAVs under Physical Attackscited by 1
  34. 2025Action Robust Reinforcement Learning via Optimal Adversary Aware Policy Optimizationcited by 1
  35. 2025Empirical Study on Robustness and Resilience in Cooperative Multi-Agent Reinforcement Learningcited by 1
  36. 2022BARReL: Bottleneck Attention for Adversarial Robustness in Vision-Based Reinforcement Learningcited by 0
  37. 2023Robust Adversarial Attacks Detection for Deep Learning based Relative Pose Estimation for Space Rendezvouscited by 0
  38. 2024Lipschitz-Regularized Critics Lead to Policy Robustness Against Transition Dynamics Uncertaintycited by 0

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