Adversarial Testing with Reinforcement Learning
Andrea Doreste · 2025
Ensuring the proper behavior of autonomous systems, such as Autonomous Driving Systems (ADSs), is essential to ensure their safety. However, testing them effectively and efficiently is still an open research challenge. Existing testing techniques rely on simulations and manipulate objects in the virtual environment to trigger the misbehavior of the system under test. Techniques such as Reinforcement Learning (RL) have been applied to effectively modify static or dynamic objects of the environment, such as properties of obstacles and behaviors of pedestrians or other vehicles. However, these approaches implement centralized controllers of the environment, resulting in possibly unrealistic and even invalid failures of the system. Considering these limitations, in my Ph.D., I aim to use RL to create adversarial agents that are fully autonomous, independent, and act to challenge the ADS under test, finding failures in critical scenarios, and contributing to improve the robustness of the ADS under test.