Research on Node Deployment in Different Terrain of MANET Based on Relational Deep Reinforcement Learning

Abstract

The deployment of mobile ad hoc network communication nodes is similar to the game process of go black and white. Using the deep reinforcement learning technology in AlphaZero algorithm, the deployment process of mobile ad hoc network nodes is abstracted as a chess game, and the deployment process is regarded as a direct game process between communication nodes and users. The difficulty and difference are that the chess board of the chess game does not change, while the grid application of mobile ad hoc network. The geographical environment changes with different application scenarios, and affects the deployment of communication nodes. This paper studies the deep reinforcement learning technology framework based on AlphaZero algorithm, and proposes that the relationship between geographical environment and communication node deployment can be implicitly deduced through the relationship reasoning technology, so as to solve the problem of node deployment in different terrain scenarios.