SC543 - Decision Intelligence for Optical Networking
07 Mar 2027
13:30 - 17:30
Short Course Level
Beginner
Short Course Description
Decision intelligence combining data-driven models, optimization, and autonomous agents, has gained significant attention due to its ability to handle complex environments and reasoning chains, such as those found in optical networks. Unlike traditional algorithms that follow predefined rules (heuristics), learning-based methods learn strategies, called policies, that aim to maximize long-term performance. Agentic AI goes further: agents reason, plan, and call tools to pursue goals with limited to no human supervision, adapting as conditions change. This course explores how these techniques can be applied to challenges in optical networking, such as resource efficiency and fault recovery. We begin with a survey of recent research that demonstrates how learning-based and agentic solutions can outperform traditional approaches in this domain, or enable autonomous use cases not possible before. The course then introduces the fundamental concepts of reinforcement learning (agents, environments, states, actions, policies, and rewards) before focusing on agentic AI, including large language model agents, tool use, and planning. Hands-on activities let participants work with an agent that interacts with a digital twin of an optical network. The goal is to provide participants with a clear and accessible foundation in decision intelligence, enabling them to understand current applications and explore how these techniques might benefit their own research.
Short Course Benefits
This course will enable participants to:
- Understand the main concepts related to DRL.
- Identify which category of problems can be tackled using DRL.
- Discuss the current state-of-the-art of DRL for optical networking.
- Discuss a proof-of-concept DRL environment for optical networking.
- Test proof-of-concept DRL agents for resource allocation and admission control
- use cases.
- Define, describe, and measure relevant metrics that assess the performance of
- the tested agents.
The participants will gain access to a working prototype for the hands-on part. They will have a guided overview of the code and be able to exercise a few aspects of DRL environments and agents.
Short Course Audience
The course is aimed at professionals from academia or industry without any previous knowledge on machine learning or reinforcement learning. Basic understanding of resource assignment problems in optical networking is recommended but not required. Attendance can also be beneficial for attendees with knowledge on machine learning for optical networking, but who want to extend their knowledge in the specifics of DRL.
Instructors
-
Carlos Natalino
Chalmers University of Technology, Sweden
Carlos Natalino is a Researcher with the Department of Electrical Engineering, Chalmers University of Technology, Gothenburg Sweden. His research interests include applying machine learning techniques to the design and operation of telecommunication
infrastructures. Carlos has been involved in several national and international research projects funded by European, Swedish, and Brazilian funding bodies. In addition, he has nine years of experience in designing and teaching courses related to computer networks and programming. He is the author of the Optical Networking Gym, an open- source tool to assist on benchmarking resource assignment algorithms. He is a member of Optica and a Senior Member of IEEE. -
Sebastian Troia
Polytechnic of Milan, Italy
Sebastian Troia is a Tenure-Track Assistant Professor with the Department of Electronics, Information and Bioengineering (DEIB) at Politecnico di Milano and a Fulbright research scholar with the University of Texas Dallas (USA). His current research interests are in the field of edge network softwarization and Machine Learning for communication networks. His activity regards the development of intelligent control and orchestration plane architectures for SDN and SD-WAN in multi-layer (optical and IP) network scenarios. He was involved in different European Projects. He serves as Reviewer of several international journals and he is in the Technical Program
Committee of international conferences and workshops. He is co-organizer of the 4th Edge Network Softwarization workshop co-located at NetSoft conference 2025.