Abstract: Autonomous Driving Systems (ADS) are inherently safety-critical applications and must be thoroughly tested before deployment. One way to achieve this is by using Reinforcement Learning (RL) to dynamically test the ADS behaviors by controlling Non-Playable Characters (NPCs) as adversarial agents. In this approach, the RL adversarial agent is guided by a reward function to learn how to challenge the original behavior of ADS under test. However, creating and fine-tuning a reward function requires domain expertise and manual experimentation. To address this, we propose using Population-Based Training (PBT).

Bonus: Rio de Janeiro – a beginner’s guide
Since ICSE 2026 is happening in Rio de Janeiro, my hometown, I thought it would be a good opportunity to share some information about the city with people who are planning to travel there.

Chair: Marco Raglianti