Open AccessResearch on Application of LSTM-QDN in Intelligent Air Combat Simulation
Author(s) -
Dongyuan Hu,
Jialiang Zuo,
Wei Zheng,
Ze Zhang,
Ya-Pu Zhao,
Qiang Zhang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1746/1/012028
Subject(s) - reinforcement learning , computer science , process (computing) , artificial intelligence , virtual machine , air combat , kinematics , virtual actor , state space , space (punctuation) , function (biology) , intelligent agent , human–computer interaction , simulation , virtual reality , statistics , physics , mathematics , classical mechanics , evolutionary biology , biology , operating system
Aiming at the problem of the lack of intelligence of virtual machine opponents in the human-machine confrontation semi-physical simulation environment, it is proposed to apply the deep reinforcement learning method into tactical making-decision for building an AI virtual pilot with self-confrontation and self-learning ability. First, flight dynamics and kinematics are used to build basic flight models in the simulation environment, and a missile attack area is established for weapon model; Second, inspired by the framework of interaction between the agent and the environment in reinforcement learning, a tactical decision architecture for flight agent based on the one-to-one tactical confrontation process is organized. Finally, the improved DQN method is used to fit the value function in the continuous state space, and the network training is completed by means of agent self-antagonism and human-machine confrontation. the well-trained AI model can undertake the role of virtual opponents in human-machine confrontation environment, and shows a certain degree of intelligence in the confrontation process with pilots.