Journal of System Simulation
Abstract
Abstract: Aiming at the insufficient solving speed of assignment strategy optimization algorithm in largescale scenarios, deep reinforcement learning is combined with Markov decision process to carry out the intelligent large-scale air defense task assignment. According to the characteristics of large-scale air defense operations, Markov decision process is used to model the agent and a digital battlefield simulation environment is built. Air defense task assignment agent is designed and trained in digital battlefield simulation environment through proximal policy optimization algorithm. The feasibility and advantage of the method are verified by taking a large-scale ground-to-air countermeasure mission as an example.
Recommended Citation
Liu, Jiayi; Wang, Gang; Fu, Qiang; Guo, Xiangke; and Wang, Siyuan
(2023)
"Intelligent Air Defense Task Assignment Based on Assignment Strategy Optimization Algorithm,"
Journal of System Simulation: Vol. 35:
Iss.
8, Article 7.
DOI: 10.16182/j.issn1004731x.joss.22-0432
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol35/iss8/7
First Page
1705
Last Page
1716
CLC
TP391.9
Recommended Citation
Liu Jiayi, Wang Gang, Fu Qiang, et al. Intelligent Air Defense Task Assignment Based on Assignment Strategy Optimization Algorithm[J]. Journal of System Simulation, 2023, 35(8): 1705-1716.
DOI
10.16182/j.issn1004731x.joss.22-0432
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