Towards Explainable Reinforcement Learning in Optical Networks: The RMSA Use Case

We propose an approach to extract explanations from a trained reinforcement learning agent. Our analysis over three RMSA environment variations shows how the agent uses the input information, increasing our understanding of its learned policy.