👉 Qualification computing, often abbreviated as QC, is a method used to evaluate and validate the performance of artificial intelligence (AI) systems, particularly in reinforcement learning. It involves a two-step process: first, a system is trained to perform a task using a standard reinforcement learning algorithm, and then it is assessed on its ability to generalize beyond the training environment. This is typically done by comparing its performance on a "test" environment, which differs from the training setting in terms of dynamics, objectives, and constraints. The goal is to ensure that the AI system can adapt effectively to new, unseen scenarios, demonstrating robustness and flexibility beyond its initial training. This approach is crucial for deploying AI systems in real-world applications where conditions are unpredictable and varied.
