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Robust Reinforcement Learning via Adversarial training with Langevin Dynamics

arXiv 2002.06063 ↗First posted 14 Feb 2020cs.LG stat.MLcited by 45

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Abstract (arXiv)

We introduce a sampling perspective to tackle the challenging task of training robust Reinforcement Learning (RL) agents. Leveraging the powerful Stochastic Gradient Langevin Dynamics, we present a novel, scalable two-player RL algorithm, which is a sampling variant of the two-player policy gradient method. Our algorithm consistently outperforms existing baselines, in terms of generalization across different training and testing conditions, on several MuJoCo environments. Our experiments also show that, even for objective functions that entirely ignore potential environmental shifts, our sampling approach remains highly robust in comparison to standard RL algorithms.

Key facts. Cited by 45 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Feb 2020.

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Citing papers per year: 2020: 1; 2021: 7; 2022: 11; 2023: 8; 2024: 6; 2025: 10; 2026: 2.

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  1. 2021Maximum Entropy RL (Provably) Solves Some Robust RL Problemscited by 169
  2. 2022RORL: Robust Offline Reinforcement Learning via Conservative Smoothingcited by 83
  3. 2021Policy Smoothing for Provably Robust Reinforcement Learningcited by 39
  4. 2022Probabilistically Robust Learning: Balancing Average- and Worst-case Performancecited by 36
  5. 2020Robust Inverse Reinforcement Learning under Transition Dynamics Mismatchcited by 33
  6. 2021Robust Predictable Controlcited by 29
  7. 2022Robust Reinforcement Learning as a Stackelberg Game via Adaptively-Regularized Adversarial Trainingcited by 26
  8. 2022No-Regret Learning in Games with Noisy Feedback: Faster Rates and Adaptivity via Learning Rate Separationcited by 17
  9. 2022Identifiability and generalizability from multiple experts in Inverse Reinforcement Learningcited by 16
  10. 2021Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learningcited by 14
  11. 2022Max-Min Off-Policy Actor-Critic Method Focusing on Worst-Case Robustness to Model Misspecificationcited by 11
  12. 2025Integrating Reinforcement Learning with Visual Generative Models: Foundations and Advancescited by 11
33 more
  1. 2022Robust Learning from Observation with Model Misspecificationcited by 9
  2. 2023Robust Adversarial Reinforcement Learning via Bounded Rationality Curriculacited by 9
  3. 2023MICRO: Model-Based Offline Reinforcement Learning with a Conservative Bellman Operatorcited by 9
  4. 2022Robust Reinforcement Learning in Continuous Control Tasks with Uncertainty Set Regularizationcited by 7
  5. 2023ReRoGCRL: Representation-based Robustness in Goal-Conditioned Reinforcement Learningcited by 6
  6. 2021Learning a Robust Multiagent Driving Policy for Traffic Congestion Reductioncited by 4
  7. 2024Adversarially Robust Decision Transformercited by 4
  8. 2026ADV-0: Closed-Loop Min-Max Adversarial Training for Long-Tail Robustness in Autonomous Drivingcited by 3
  9. 2021ACReL: Adversarial Conditional value-at-risk Reinforcement Learningcited by 2
  10. 2021Convergence of GANs Training: A Game and Stochastic Control Methodologycited by 2
  11. 2022Review of Metrics to Measure the Stability, Robustness and Resilience of Reinforcement Learningcited by 2
  12. 2023Robust Reinforcement Learning through Efficient Adversarial Herdingcited by 2
  13. 2024Context-aware Diversity Enhancement for Neural Multi-Objective Combinatorial Optimizationcited by 2
  14. 2024On Minimizing Adversarial Counterfactual Error in Adversarial RLcited by 2
  15. 2024RRLS : Robust Reinforcement Learning Suitecited by 2
  16. 2023Provable Robustness for Streaming Models with a Sliding Windowcited by 1
  17. 2024Solving robust MDPs as a sequence of static RL problemscited by 1
  18. 2025CAMP in the Odyssey: Provably Robust Reinforcement Learning with Certified Radius Maximizationcited by 1
  19. 2025Layer-wise Quantization for Quantized Optimistic Dual Averagingcited by 1
  20. 2025Curriculum-Adapted Robust Reinforcement Learning for UAV Deconfliction in Adversarial Environmentscited by 1
  21. 2022User-Oriented Robust Reinforcement Learningcited by 0
  22. 2022Quantification before Selection: Active Dynamics Preference for Robust Reinforcement Learningcited by 0
  23. 2023On-line reinforcement learning for optimization of real-life energy trading strategycited by 0
  24. 2023Robust Offline Reinforcement Learning -- Certify the Confidence Intervalcited by 0
  25. 2023Where2Start: Leveraging initial States for Robust and Sample-Efficient Reinforcement Learningcited by 0
  26. 2024Zero-Sum Positional Differential Games as a Framework for Robust Reinforcement Learning: Deep Q-Learning Approachcited by 0
  27. 2025Deep Learning Agents Trained For Avoidance Behave Like Hawks And Dovescited by 0
  28. 2025Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluationcited by 0
  29. 2025Meta Policy Switching for Secure UAV Deconfliction in Adversarial Airspacecited by 0
  30. 2025Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validationcited by 0
  31. 2025On the Tension Between Optimality and Adversarial Robustness in Policy Optimizationcited by 0
  32. 2025UACER: An Uncertainty-Adaptive Critic Ensemble Framework for Robust Adversarial Reinforcement Learningcited by 0
  33. 2026Robust Adversarial Reinforcement Learning with Risk Sensitivity and Critic Consistency Regularizationcited by 0