1 code implementation • 18 Feb 2022 • Baoqian Wang, Junfei Xie, Nikolay Atanasov
In this paper, we address this limitation by introducing a scalable MARL method called Distributed multi-Agent Reinforcement Learning with One-hop Neighbors (DARL1N).
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 15 Apr 2021 • Baoqian Wang, Junfei Xie, Kejie Lu, Yan Wan, Shengli Fu
Mobile ad hoc computing (MAHC), which allows mobile devices to directly share their computing resources, is a promising solution to address the growing demands for computing resources required by mobile devices.
no code implementations • 7 Jan 2021 • Baoqian Wang, Junfei Xie, Nikolay Atanasov
This paper aims to mitigate straggler effects in synchronous distributed learning for multi-agent reinforcement learning (MARL) problems.
Multi-agent Reinforcement Learning reinforcement-learning +1
1 code implementation • 29 Dec 2019 • Baoqian Wang, Junfei Xie, Kejie Lu, Yan Wan, Shengli Fu
In recent years, coded distributed computing (CDC) has attracted significant attention, because it can efficiently facilitate many delay-sensitive computation tasks against unexpected latencies in distributed computing systems.
Distributed, Parallel, and Cluster Computing