  Resource allocation has a direct and profound impact on the performance of
vehicle-to-everything (V2X) networks. In this paper, we develop a hybrid
architecture consisting of centralized decision making and distributed resource
sharing (the C-Decision scheme) to maximize the long-term sum rate of all
vehicles. To reduce the network signaling overhead, each vehicle uses a deep
neural network to compress its observed information that is thereafter fed back
to the centralized decision making unit. The centralized decision unit employs
a deep Q-network to allocate resources and then sends the decision results to
all vehicles. We further adopt a quantization layer for each vehicle that
learns to quantize the continuous feedback. In addition, we devise a mechanism
to balance the transmission of vehicle-to-vehicle (V2V) links and
vehicle-to-infrastructure (V2I) links. To further facilitate distributed
spectrum sharing, we also propose a distributed decision making and spectrum
sharing architecture (the D-Decision scheme) for each V2V link. Through
extensive simulation results, we demonstrate that the proposed C-Decision and
D-Decision schemes can both achieve near-optimal performance and are robust to
feedback interval variations, input noise, and feedback noise.
