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.

本文基于深度强化学习，开发了一种分散资源分配机制，应用于车对车（V2V）通信中，在无单播和广播场景下均可使用。根据分散的资源分配机制，一个自治体、一个 V2V 链路或一辆车，可以在不要求或等待全局信息的情况下，做出决策，查找传输的最佳子带宽和功率水平。由于所提出的方法是分散的，因此仅产生有限的传输开销。根据仿真结果，每个自治体可以有效地学习，以满足对 V2V 链路的严格延迟约束，同时将干扰降至最低。

基于深度强化学习的 V2V 通信资源分配

Deep Reinforcement Learning based Resource Allocation for V2V  Communications

In this article, we develop a decentralized resource allocation mechanism for
vehicle-to-vehicle (V2V) communication systems based on deep reinforcement
learning. Each V2V link is considered as an agent, making its own decisions to
find optimal sub-band and power level for transmission. Since the proposed
method is decentralized, the global information is not required for each agent
to make its decisions, hence the transmission overhead is small. From the
simulation results, each agent can learn how to satisfy the V2V constraints
while minimizing the interference to vehicle-to-infrastructure (V2I)
communications.

本文提出了一种基于深度强化学习的分散式资源分配机制，以优化车对车通信系统的信道和功率分配，同时减小对车对基础设施通信系统的干扰。