The construction of mobile ad-hoc networks on the battlefield is mainly planned by staff or automatically planned with the help of network topology planning models in the network planning software. Most of these algorithms are actually more or less based on human knowledge or thinking ways to model network entities, environments, and rules, and the accuracy and rationality of models are not strong enough to match the changed battlefield. The AlphaZero algorithm generalised in chess and other games provides a new intelligent method to solve complex problems in the military field. Based on the AlphaZero algorithm, this study proposes a method for intelligent deployment of mobile ad-hoc networks with tactical communication node vehicles. Making an analogy between deploying tactical communication node vehicles and playing Go, the authors construct a deep reinforcement learning model for deployment of communication node vehicles. Starting from random play, and giving no domain knowledge, only setting the judgment of the network structure, with training the designing strategy value deep neural network by self-play reinforcement learning, they successfully deployed communication node vehicles on tabula rasa map and constructed battlefield mobile ad-hoc networks with deep reinforcement learning and Monte–Carlo tree search.

中文翻译：

---

使用AlphaZero算法部署战术通信节点车辆

战场上的移动自组织网络的构建主要是由工作人员计划的，或者是借助网络计划软件中的网络拓扑计划模型自动计划的。这些算法中的大多数实际上或多或少地基于人类知识或思维方式来对网络实体，环境和规则进行建模，并且模型的准确性和合理性不足以匹配变化的战场。国际象棋和其他游戏中普遍使用的AlphaZero算法为解决军事领域的复杂问题提供了一种新的智能方法。基于AlphaZero算法，本研究提出了一种战术通信节点车辆对移动自组织网络进行智能部署的方法。打个比方，部署战术通信节点车辆和玩Go，作者构建了用于通信节点车辆部署的深度强化学习模型。从随机游戏开始，不提供任何领域知识，仅设置网络结构的判断力，通过自我博弈强化学习训练设计策略价值的深层神经网络，他们成功地将通信节点车辆部署在塔布拉拉萨地图上并建造了战场移动平台具有深度强化学习和蒙特卡洛树搜索功能的临时网络。