Graph Transformers and Stabilized Reinforcement Learning for Large-Scale Dynamic Routing Modulation and Spectrum Allocation in Elastic Optical Networks

2026-05-03

AI Summary

This work addresses the lack of scalable and stable reinforcement learning solutions for the routing, modulation, and spectrum assignment (RMSA) problem in large-scale dynamic optical networks. It proposes the first application of the Transformer architecture to dynamic RMSA, integrating graph-structured rotary position encoding, off-policy invalid-action masking, and effective-quality regularization to establish a robust and efficient reinforcement learning training framework. Evaluated on a real-world network topology with 143 nodes and 362 links, the method supports up to 13% additional traffic load and achieves a 4% improvement over the best-performing baseline while maintaining a blocking probability below 0.1%. The authors release all code publicly to facilitate reproducibility and future research.

Abstract

Reinforcement learning (RL) has been widely applied to dynamic routing, modulation and spectrum assignment (RMSA) in optical networks, yet no prior work has trained a transformer model for this task. We attribute this to the high data and compute requirements of transformers and potential training instabilities with RL. We address this gap by combining recent advances from the machine learning literature (rotary positional encodings for graph-structured data, off-policy invalid action masking, and valid mass regularization) with GPU-accelerated simulation to achieve, for the first time, stable RL training of a transformer for dynamic RMSA. We demonstrate, through systematic benchmarking against previous RL methods and heuristic algorithms, that ours is the first RL method to exceed all benchmarks, increasing the supportable traffic load by up to 13\%. To demonstrate the scalability of our approach, we train on real network topologies from the TopologyBench database up to 143 nodes and 362 links, with 320 x 12.5\,GHz frequency slot units per link, and 100\,Gbps traffic requests. To our knowledge, these are the largest dynamic RMSA problems to which RL has been applied. We find up to 4\% increased traffic load can be supported at low blocking probability (<0.1\%) with our method compared to the best available benchmark algorithm. We present an ablation study of the components of our training algorithm, the dynamics of the loss function during training, and analyze the allocation decisions of the trained models. We make all code used to produce this paper openly available for reproduction and future benchmarking: https://github.com/micdoh/XLRON.

Problem

• Dynamic RMSA
• Elastic Optical Networks
• Reinforcement Learning
• Graph Transformers
• Large-Scale Optimization

Innovation

• Graph Transformers
• Stabilized Reinforcement Learning
• Dynamic RMSA
• Rotary Positional Encodings
• Invalid Action Masking