Synthesis and Verification of Neural Control Barrier Functions for Safe Reinforcement Learning with Guarantees

While learning-based control techniques often outperform classical controller designs, safety requirements limit the acceptance of such methods in many applications. Recent developments address this issue through Certified Learning (CL), which combines a learning-based controller with formal methods to provide safety guarantees. This thesis...

Learning Interaction-Aware Guidance for Trajectory Optimization in Dense Traffic Scenarios

Autonomous navigation in dense traffic scenarios remains challenging for autonomous vehicles (AVs) because the intentions of other drivers are not directly observable and AVs have to deal with a wide range of driving behaviors. To maneuver through dense traffic, AVs must be able to reason how their actions affect others (interaction model)...

Safe Curriculum Learning for Linear Systems With Unknown Dynamics in Primary Flight Control

Safe Curriculum Learning constitutes a collection of methods that aim at enabling Rein- forcement Learning (RL) algorithms on complex systems and tasks whilst considering the safety and efficiency aspect of the learning process. On the one hand, curricular reinforce- ment learning approaches divide the task into more gradual complexity stages to...

Safe Curriculum Learning for Primary Flight Control

Reinforcement learning (RL) enables the autonomous formation of optimal, adaptive control laws for systems with complex, uncertain dynamics. This process generally requires a learning agent to directly interact with the system in an online fashion. However, if the system is safety-critical, such as an Unmanned Aerial Vehicle (UAV), learning may...