Multi-Agent Deep Reinforcement Learning for Resource Allocation over V2x Networks(2/3)

Project Details

Description

In the trend of the Internet of Things (IoT), vehicle-to-everything (V2X), and edge computing, vehicles can be considered as smart agents collaborating to provide services through shared communication, storage, and computing resources. The multi-agent reinforcement learning (MARL) technique can be a proper tool to analyze V2X resource allocation issues. The multi-agent system formed by vehicles can be utilized to achieve distributed decisions through edge computing architecture. However, the solution to this topic is still emerging and worth further study. In this proposal, we plan to begin with a scalable MAS architecture, and then investigate how to deal with mobility and information sharing issues using transfer learning and partially observable Markov decision process (POMDP), respectively. Applications such as streaming and mission-critical transmission will be considered. The effectiveness of MARL will be investigated and target for innovative resource allocation toward future V2X applications.
StatusActive
Effective start/end date1/08/2131/07/22

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

  • SDG 8 - Decent Work and Economic Growth
  • SDG 12 - Responsible Consumption and Production
  • SDG 17 - Partnerships for the Goals

Keywords

  • V2X
  • resource allocation
  • reinforcement learning
  • deep learning
  • transfer learning
  • POMDP

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