Multi-Agent Deep Reinforcement Learning for Channel Resource Allocation in Vehicular Networks

Fuxin Zhang; Rui Jiang

doi:10.2139/ssrn.4188679

ScienceGate Book Chapters

JOURNAL ARTICLE

Multi-Agent Deep Reinforcement Learning for Channel Resource Allocation in Vehicular Networks

Fuxin Zhang Rui Jiang

Year: 2022 Journal: SSRN Electronic Journal Publisher: RELX Group (Netherlands)

DOI: 10.2139/ssrn.4188679

Get Full-Text PDF Get Analytical Report

Keywords:

Reinforcement learning Resource allocation Computer science Reinforcement Channel (broadcasting) Artificial intelligence Computer network Psychology Social psychology

Metrics

Cited By

0.00

FWCI (Field Weighted Citation Impact)

Refs

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Topics

Vehicular Ad Hoc Networks (VANETs)

Physical Sciences → Engineering → Electrical and Electronic Engineering

Power Line Communications and Noise

Physical Sciences → Engineering → Electrical and Electronic Engineering

Advanced MIMO Systems Optimization

Physical Sciences → Engineering → Electrical and Electronic Engineering

Multi-Agent Deep Reinforcement Learning for Channel Resource Allocation in Vehicular Networks

Metrics

Topics

Related Documents

Multi-Agent Deep Reinforcement Learning for Channel Resource Allocation in Vehicular Networks

Non-cooperative multi-agent deep reinforcement learning for channel resource allocation in vehicular networks

Multi-Agent Deep Reinforcement Learning-Empowered Channel Allocation in Vehicular Networks

Multi-agent reinforcement learning based resource allocation for vehicular networks

Resource allocation strategy for vehicular communication networks based on multi-agent deep reinforcement learning