Safe Reinforcement Learning: Providing Task-Agnostic Reach-Avoid Safety Constraints for Drone Deployment

Abstract

Drones are increasingly employed in critical applications, yet ensuring their safe operation in dynamic and unpredictable environments remains a challenge. This thesis examines the use of reach-avoid reinforcement learning (RL) for developing a task-agnostic safety filter for drones, with a focus on theoretical guarantees, practical applications, and future directions. By integrating safety constraints directly into the learning process, reach-avoid RL offers a robust and scalable framework for navigating the complexities of real-world safety scenarios. The reach-avoid safety filter, in combination with deep reinforcement learning and game-theoretic approaches, offers a feasible method for safe reinforcement learning across a range of tasks and environments in drone deployment.