Deploying tactical communication node vehicles with AlphaZero algorithm

Abstract

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.