PDAA3C: An A3C-Based Multi-Path Data Scheduling Algorithm - WU, Chengyu; WANG, Zhengqiang; LIANG, Teng; ZHAN, Ao (IEICE Transactions on Information and Systems, 2022)
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.