Multiagent deep reinforcement learning based resource management technique for vehicular networks in heterogeneous traffic

Key Points

• To develop a multiagent deep reinforcement learning technique for effective resource management in vehicular networks under varying traffic conditions.
• Utilized multiagent systems to manage resources in vehicular networks.
• Applied deep reinforcement learning algorithms to optimize decision-making.
• Simulated heterogeneous traffic scenarios to evaluate effectiveness.
• Demonstrated improved resource allocation efficiency compared to traditional methods.
• Showed a significant reduction in latency within the network.
• Found enhanced performance in managing varying traffic loads.