mlsanityData to 14 Oct 2026

Failure-Scenario Maker for Rule-Based Agent using Multi-agent Adversarial Reinforcement Learning and its Application to Autonomous Driving

arXiv 1903.10654 ↗First posted 26 Mar 2019cs.LG cs.AI cs.ROcited by 36

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Abstract (arXiv)

We examine the problem of adversarial reinforcement learning for multi-agent domains including a rule-based agent. Rule-based algorithms are required in safety-critical applications for them to work properly in a wide range of situations. Hence, every effort is made to find failure scenarios during the development phase. However, as the software becomes complicated, finding failure cases becomes difficult. Especially in multi-agent domains, such as autonomous driving environments, it is much harder to find useful failure scenarios that help us improve the algorithm. We propose a method for efficiently finding failure scenarios; this method trains the adversarial agents using multi-agent reinforcement learning such that the tested rule-based agent fails. We demonstrate the effectiveness of our proposed method using a simple environment and autonomous driving simulator.

Key facts. Cited by 36 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 Mar 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 5; 2022: 2; 2023: 9; 2024: 8; 2025: 9; 2026: 3.

36 papers.

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  1. 2021AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehiclescited by 137
  2. 2024ChatScene: Knowledge-Enabled Safety-Critical Scenario Generation for Autonomous Vehiclescited by 93
  3. 2023CAT: Closed-loop Adversarial Training for Safe End-to-End Drivingcited by 54
  4. 2021Vehicle trajectory prediction works, but not everywherecited by 39
  5. 2025Multi-Agent Coordination across Diverse Applications: A Surveycited by 37
  6. 2023The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Reviewcited by 29
  7. 2021A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulationcited by 21
  8. 2024A Review of Reward Functions for Reinforcement Learning in the context of Autonomous Drivingcited by 18
  9. 2023Safety-Critical Scenario Generation Via Reinforcement Learning Based Editingcited by 16
  10. 2024Adversarial Safety-Critical Scenario Generation using Naturalistic Human Driving Priorscited by 15
  11. 2024Learning to Drive via Asymmetric Self-Playcited by 11
  12. 2021Adversarial Deep Reinforcement Learning for Improving the Robustness of Multi-agent Autonomous Driving Policiescited by 10
24 more
  1. 2023Adv3D: Generating Safety-Critical 3D Objects through Closed-Loop Simulationcited by 10
  2. 2025Steerable Adversarial Scenario Generation through Test-Time Preference Alignmentcited by 9
  3. 2023(Re)2H2O: Autonomous Driving Scenario Generation via Reversely Regularized Hybrid Offline-and-Online Reinforcement Learningcited by 6
  4. 2023From Model-Based to Data-Driven Simulation: Challenges and Trends in Autonomous Drivingcited by 6
  5. 2023Stackelberg Driver Model for Continual Policy Improvement in Scenario-Based Closed-Loop Autonomous Drivingcited by 3
  6. 2024Adversarial and Reactive Traffic Entities for Behavior-Realistic Driving Simulation: A Reviewcited by 3
  7. 2026ADV-0: Closed-Loop Min-Max Adversarial Training for Long-Tail Robustness in Autonomous Drivingcited by 3
  8. 2022ECSAS: Exploring Critical Scenarios from Action Sequence in Autonomous Drivingcited by 2
  9. 2022A Bibliometric Analysis and Review on Reinforcement Learning for Transportation Applicationscited by 2
  10. 2024Learning to Model Diverse Driving Behaviors in Highly Interactive Autonomous Driving Scenarios with Multi-Agent Reinforcement Learningcited by 2
  11. 2025AED: Automatic Discovery of Effective and Diverse Vulnerabilities for Autonomous Driving Policy with Large Language Modelscited by 2
  12. 2021Multi-Agent Vulnerability Discovery for Autonomous Driving with Hazard Arbitration Rewardcited by 1
  13. 2025Towards Benchmarking and Assessing the Safety and Robustness of Autonomous Driving on Safety-critical Scenarioscited by 1
  14. 2025Embodied Intelligence: The Key to Unblocking Generalized Artificial Intelligencecited by 1
  15. 2025An Evolving Scenario Generation Method based on Dual-modal Driver Model Trained by Multi-Agent Reinforcement Learningcited by 1
  16. 2023Zero-shot Transfer Learning of Driving Policy via Socially Adversarial Traffic Flowcited by 0
  17. 2023ReMAV: Reward Modeling of Autonomous Vehicles for Finding Likely Failure Eventscited by 0
  18. 2024Acceleration method for generating perception failure scenarios based on editing Markov processcited by 0
  19. 2024Foundation Models for Rapid Autonomy Validationcited by 0
  20. 2025Position: Emergent Machina Sapiens Urge Rethinking Multi-Agent Paradigmscited by 0
  21. 2025Diverse and Adaptive Behavior Curriculum for Autonomous Driving: A Student-Teacher Framework with Multi-Agent RLcited by 0
  22. 2025DeepForgeSeal: Latent Space-Driven Semi-Fragile Watermarking for Deepfake Detection Using Adversarial Reinforcement Learningcited by 0
  23. 2026ScenePilot: Controllable Boundary-Driven Critical Scenario Generation for Autonomous Drivingcited by 0
  24. 2026Teach-to-Crash: A Closed-Loop Student-Teacher LLM Framework for Collision-Inducing Test Scenario Generationcited by 0