Abstract: In response to the problem of the difficulty of decision-making in the game of force under the constraints of high-dimensional state-space in multi-machine air combat confrontation scenarios, a force intelligent agent decision-making generation strategy based on deep reinforcement learning is adopted. The developing situational cognition and reward feedback generation algorithms for force intelligentgame are proposed, a behavior modeling hierarchical framework based on hybrid intelligence modeling method is constructed, which solve the technical difficulty of sparse reward in the reinforcement learning process. It provides an feasible reinforcement learning training method that can solve the large-scale, multi-model, and multi-element air combat problems.
Wang, Yukun; Wang, Ze; Dong, Liwei; and Li, Ni
"Research on Multi-aircraft Air Combat Behavior Modeling Based on Hierarchical Intelligent Modeling Methods,"
Journal of System Simulation: Vol. 35:
10, Article 16.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol35/iss10/16
Wang Yukun, Wang Ze, Dong Liwei, et al. Research on Multi-aircraft Air Combat Behavior Modeling Based on Hierarchical Intelligent Modeling Methods[J]. Journal of System Simulation, 2023, 35(10): 2249-2261.
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