Reinforcement Learning Qualification Process

SUMMARY & CONCLUSIONSAs reinforcement learning (RL) continues to gain traction in safety-critical applications, the need for rigorous evaluation methods that ensure reliability, maintainability, and safety becomes paramount. This paper introduces the Reinforcement Learning Qualification Process (RLQP), a comprehensive framework designed to systematically evaluate and qualify RL algorithms for deployment in safety-critical systems. RLQP addresses the gap between theoretical RL performance and real-world reliability requirements by incorporating standardized perturbation testing, statistical robustness bounds, and failure mode analysis. We demonstrate RLQP with autonomous drone landing, revealing critical insights into algorithm behavior under realistic environmental and sensor perturbations. Our results show that without appropriate training, traditional RL algorithms exhibit significant performance degradation under combined perturbations, with failure rates increasing by up to 60% under realistic conditions. The framework provides actionable guidelines for establishing safe operational bounds, analyzing failure modes, and improving training methodologies for safe and robust RL deployment.