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Multi-Agent Deep Reinforcement Learning for Connected Vehicles (poster)

The vehicles take a role of mobile devices so that resource management issue among them in cellular networks is getting highlighted. In addition, the millimeter-wave (mmWave) base station is expected to be included in existing heterogeneous networks. In this regard, we present a multi-agent deep reinforcement learning (MADRL) based resource allocation strategy for mobile devices in heterogeneous vehicular networks, which suffer from shortage issue of shared frequency band. The downlink throughput of each mobile device is cooperatively enhanced by the proposed MADRL method, and thus each mobile device associates with a base station and uses a frequency band within low delay.