Asynchronous Deep Reinforcement Learning for Collaborative Task Computing and On-Demand Resource Allocation in Vehicular Edge Computing

Vehicular Edge Computing (VEC) is enjoying a surge in research interest due to the remarkable potential to reduce response delay and alleviate bandwidth pressure. Facing the ever-growing service applications in VEC, how to effectively aggregate and flexibly schedule ubiquitous network resources for implementing diverse tasks and meeting differentiated demands from numerous vehicular users remains haunting. Toward this end, we investigate collaborative task computing and on-demand resource allocation. The collaborative computing framework in VEC is provided to support deep collaboration and intelligent management of heterogeneous resources widely distributed in vehicles, edge servers and cloud. Based on this…

• NN
  National Natural Science Foundation of China

  Awards: 62001357, 62102297, 62132013
• CP
  China Postdoctoral Science Foundation

  Award: 2021M692501
• NK
  National Key Research and Development Program of China

  Award: 2020YFB1807500
• KR
  Key Research and Development Projects of Shaanxi Province

  Award: 2021ZDLGY06-03
• BA
  Basic and Applied Basic Research Foundation of Guangdong Province

  Awards: 2020A1515110079, 2020A1515110496