An Improved MPQUIC Scheduler Based on Multi-Agent Reinforcement Learning

Abstract

Conventional Multipath QUIC (MPQUIC) scheduler struggles in dynamic networks with multiple clients, significantly hindering its potential. In this letter, a Multi-Agent Reinforcement Learning-based MPQUIC scheduler is designed to optimize communication transmission in dynamic networks for the multi-client scenario. The proposed scheduler is implemented on the server side with a Deep Q-Network (DQN) agent for each client, each agent observes the state of all network flows and adjusts scheduling strategies to enhance the Quality of Service (QoS) for dynamic networks. The simulation results demonstrate that the scheduler significantly outperforms existing schedulers by reducing latency and amplifying throughput for all clients, thus adeptly satisfying the QoS requirements of multiple clients.