Conventional schemes for service provisioning in next-generation elastic optical networks
(EONs) rely on rule-based policies that suffer from scalability issues and can lead to poor …

Deep reinforcement learning (DRL) enables autonomic optical networking by allowing the
network control and management systems to self-learn successful networking policies from …

The introduction of futuristic and challenging use cases of 5G and 6G communications will
demand strict requirements in terms of high bandwidth and low latency. Optical backbone …

Deep-RMSA: A Deep-Reinforcement-Learning Routing, Modulation and Spectrum Assignment
Agent for Elastic Optical Networks Page 1 W4F.2.pdf OFC 2018 © OSA 2018 Deep-RMSA: A …

This paper proposes DeepRMSA, a deep reinforcement learning framework for routing,
modulation and spectrum assignment (RMSA) in elastic optical networks (EONs) …

This paper presents a self-driving networking paradigm exploiting the state-of-the-art
reinforcement learning and transfer learning algorithms for highly resource-efficient …

To improve the network scalability, a large elastic optical network is typically segmented into
multiple autonomous domains, where each domain possesses high autonomy and privacy …

Deep reinforcement learning (DRL) has been introduced to the routing and spectrum
assignment (RSA) of elastic optical networks (EONs) where the RSA policies are learnt …

DeepCoop: Leveraging Cooperative DRL Agents to Achieve Scalable Network Automation for
Multi-Domain SD-EONs Page 1 DeepCoop: Leveraging Cooperative DRL Agents to Achieve …

The latest advances in Deeper Reinforcement Learning (DRL) have completely changed
how decision-making and automatic control issues are solved. The study community …