航空兵器2025，Vol.32Issue(3)：48-56,9.DOI:10.12132/ISSN.1673-5048.2025.0020
远海多智能体空中对抗深度强化学习环境模型构建
Construction of a Parallel Training Environment Model for Multi-Agent Deep Reinforcement Learning in Far-Sea Aerial Confrontation
摘要
Abstract
The quality of the environment model determines whether the deep reinforcement learning system can efficiently and accurately learn and train to make good decisions.Aiming at the problems of idealized air combat envi-ronment construction and task scenarios in the context of far-sea and remote combat,this paper constructs a parallel training environment for multi-agent deep reinforcement learning in far-sea air combat.Among them,based on JSBSim and scalable radar and weapon system models,an agent model is built that takes into account both actual combat and simulation performance.This study selects 18-dimensional state space and 7-dimensional action space,and constructs a multi-reward system with the main line and 10 sub-objectives.This approach solves the problems of algorithm difficul-ty in convergence caused by poor guidance of sparse rewards and high dimensional space.The compliance of the envi-ronment,the effectiveness of classic deep reinforcement learning algorithms and compatibility with mainstream training frameworks are verified through simulation.关键词
远海远域/空中对抗/多智能体/深度强化学习/JSBSim/训练环境模型Key words
far-sea region/aerial confrontation/multi-agent/deep reinforcement learning/JSBSim/training en-vironment model分类
军事科技引用本文复制引用
张原,王江南,王伟,李璇..远海多智能体空中对抗深度强化学习环境模型构建[J].航空兵器,2025,32(3):48-56,9.基金项目
国家社会科学基金项目(2023-SKJJ-B-035) （2023-SKJJ-B-035）
