Regular Section
PDAA3C: An A3C-Based Multi-Path Data Scheduling Algorithm
2022 Volume E105.D Issue 12 Pages 2127-2130
Details
2022 Volume E105.D Issue 12 Pages 2127-2130
In this letter, a path dynamics assessment asynchronous advantage actor-critic scheduling algorithm (PDAA3C) is proposed to solve the MPTCP scheduling problem by using deep reinforcement learning Actor-Critic framework. The algorithm picks out the optimal transmitting path faster by multi-core asynchronous updating and also guarantee the network fairness. Compared with the existing algorithms, the proposed algorithm achieves 8.6% throughput gain over RLDS algorithm, and approaches the theoretic upper bound in the NS3 simulation.
