Increasingly, artificial intelligence (AI) is being used to support automotive systems, including autonomous vehicles (AVs) with self-driving capabilities. The premise is that learning-enabled systems (LESs), those systems that have one or more AI components, use statistical models to make better informed adaptation decisions and mitigate potentially dangerous situations. These AI techniques largely focus on uncertainty factors that can be explicitly identified and defined (e.g., environmental conditions). However, the unexpected behavior of human actors is a source of uncertainty that is challenging to explicitly model and define. In order to train a learning-enabled AV, developers may use a combination of real-world monitored data and simulated external actor behaviors (e.g., human-driven vehicles, pedestrians, etc.), where participants follow defined sets of rules such as traffic laws. However, if uncertain human behaviors are not sufficiently captured during training, then the AV may not be able to safely handle unexpected behavior induced by human-operated vehicles (e.g., unexpected sudden lane changes). This work introduces a non-cooperative game theory and reinforcement learning-based (RL) framework to discover and assess an AV’s ability to handle high-level uncertain behavior(s) induced by human-based rewards. The discovered synthetic data can then be used to reconfigure the AV to robustify onboard behaviors.