mlsanityData to 7 Oct 2026

Robust Deep Reinforcement Learning Through Adversarial Attacks and Training : A Survey

arXiv 2403.00420 ↗First posted 1 Mar 2024cs.LG cs.AIcited by 13

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Abstract (arXiv)

Deep Reinforcement Learning (DRL) is a subfield of machine learning for training autonomous agents that take sequential actions across complex environments. Despite its significant performance in well-known environments, it remains susceptible to minor condition variations, raising concerns about its reliability in real-world applications. To improve usability, DRL must demonstrate trustworthiness and robustness. A way to improve the robustness of DRL to unknown changes in the environmental conditions and possible perturbations is through Adversarial Training, by training the agent against well-suited adversarial attacks on the observations and the dynamics of the environment. Addressing this critical issue, our work presents an in-depth analysis of contemporary adversarial attack and training methodologies, systematically categorizing them and comparing their objectives and operational mechanisms.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Mar 2024.

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Citing papers per year: 2024: 2; 2025: 6; 2026: 5.

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  1. 2025State-Aware Perturbation Optimization for Robust Deep Reinforcement Learningcited by 7
  2. 2025Robust Deep Reinforcement Learning in Robotics via Adaptive Gradient-Masked Adversarial Attackscited by 7
  3. 2024Robust Deep Reinforcement Learning against Adversarial Behavior Manipulationcited by 2
  4. 2025Decentralized Consensus Inference-based Hierarchical Reinforcement Learning for Multi-Constrained UAV Pursuit-Evasion Gamecited by 2
  5. 2024Lipschitz-Regularized Critics Lead to Policy Robustness Against Transition Dynamics Uncertaintycited by 0
  6. 2025Dynamic Obstacle Avoidance with Bounded Rationality Adversarial Reinforcement Learningcited by 0
  7. 2025Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluationcited by 0
  8. 2025A Dynamical Systems Framework for Reinforcement Learning Safety and Robustness Verificationcited by 0
  9. 2026Sparse Threats, Focused Defense: Criticality-Aware Robust Reinforcement Learning for Safe Autonomous Drivingcited by 0
  10. 2026Reward-Preserving Attacks For Robust Reinforcement Learningcited by 0
  11. 2026Advantage-based Temporal Attack in Reinforcement Learningcited by 0
  12. 2026Taming the Adversary: A Cost-to-Disturbance Ratio Approach to Adversarial Reinforcement Learningcited by 0
1 more
  1. 2026Beyond Coefficients: Forecast-Necessity Testing for Interpretable Causal Discovery in Nonlinear Time-Series Modelscited by 0