Optimal Planning of Emergency Communication Network Using Deep Reinforcement Learning

Abstract

Aiming at the problems of traditional algorithms that require high prior knowledge and weak timeliness, this paper proposes an emergency communication network topology planning method based on deep reinforcement learning. Based on the characteristics of the emergency communication network, and drawing on chess, we map the node layout and topology planning problems in the network planning to chess game problems; The two factors of network coverage and connectivity are considered to construct the evaluation criteria for network planning; The method of combining Monte Carlo tree search and self-game is used to realize network planning sample data generation, and the network planning strategy network and value network structure based on residual network are designed. On this basis, the model was constructed and trained based on Tensorflow library. Simulation results show that the proposed planning method can effectively implement intelligent planning of network topology, and has excellent timeliness and feasibility.