Dynamic Task Allocation for Robotic Edge System Resilience Using Deep Reinforcement Learning-Reference-Cited by-同舟云学术

Dynamic Task Allocation for Robotic Edge System Resilience Using Deep Reinforcement Learning

Author:

Afrin Mahbuba¹^ORCID,Jin Jiong²^ORCID,Rahman Ashfaqur³^ORCID,Li Shi²^ORCID,Tian Yu-Chu⁴^ORCID,Li Yan⁵^ORCID

Affiliation:

1. School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Bentley, WA, Australia

2. School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, VIC, Australia

3. Data61, CSIRO, Sandy Bay, TAS, Australia

4. School of Computer Science, Queensland University of Technology, Brisbane, QLD, Australia

5. School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD, Australia

Funder

Australian Research Council Linkage Project

Data61, CSIRO

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Human-Computer Interaction,Control and Systems Engineering,Software

Link

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献