Understanding deep reinforcement learning: Enhancing explainable decision-making in optical networks

Deep Reinforcement Learning (DRL) has emerged as a promising approach for solving complex tasks in optical networks. However, its black-box nature poses challenges for interpretability. For network operators, understanding the reasoning behind decisions is crucial for effective control and resource management. This paper addresses this gap by proposing a framework that generates explanations based on DRL agents’ decision-making processes. Using imitation learning, we train four classifiers to approximate a robust DRL agent designed for elastic optical networks. Our approach enhances explainability, enabling us to better understand and manage DRL-based decisions in optical network environments.