Asynchronous MultiAgent Reinforcement Learning for 5G Routing under Side Constraints

• URL: http://arxiv.org/abs/2602.00035v1
• Date: Sun, 18 Jan 2026 18:38:37 GMT
• Title: Asynchronous MultiAgent Reinforcement Learning for 5G Routing under Side Constraints
• Authors: Sebastian Racedo, Brigitte Jaumard, Oscar Delgado, Meysam Masoudi,
• Abstract summary: We propose an asynchronous multi-agent reinforcement learning framework in which independent PPO agents plan routes in parallel and commit resource deltas to a shared global resource environment.<n>We evaluate the method on an O-RAN like network simulation using nearly real-time traffic data from the city of Montreal.<n>AMARL achieves a similar Grade of Service (GoS) and end-to-end latency, with reduced training wall-clock time and improved robustness to demand shifts.
• Score: 1.0732935873226022
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Networks in the current 5G and beyond systems increasingly carry heterogeneous traffic with diverse quality-of-service constraints, making real-time routing decisions both complex and time-critical. A common approach, such as a heuristic with human intervention or training a single centralized RL policy or synchronizing updates across multiple learners, struggles with scalability and straggler effects. We address this by proposing an asynchronous multi-agent reinforcement learning (AMARL) framework in which independent PPO agents, one per service, plan routes in parallel and commit resource deltas to a shared global resource environment. This coordination by state preserves feasibility across services and enables specialization for service-specific objectives. We evaluate the method on an O-RAN like network simulation using nearly real-time traffic data from the city of Montreal. We compared against a single-agent PPO baseline. AMARL achieves a similar Grade of Service (acceptance rate) (GoS) and end-to-end latency, with reduced training wall-clock time and improved robustness to demand shifts. These results suggest that asynchronous, service-specialized agents provide a scalable and practical approach to distributed routing, with applicability extending beyond the O-RAN domain.

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