  In this paper, we develop a decentralized resource allocation mechanism for
vehicle-to-vehicle (V2V) communications based on deep reinforcement learning,
which can be applied to both unicast and broadcast scenarios. According to the
decentralized resource allocation mechanism, an autonomous agent', a V2V link
or a vehicle, makes its decisions to find the optimal sub-band and power level
for transmission without requiring or having to wait for global information.
Since the proposed method is decentralized, it incurs only limited transmission
overhead. From the simulation results, each agent can effectively learn to
satisfy the stringent latency constraints on V2V links while minimizing the
interference to vehicle-to-infrastructure (V2I) communications.
