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| Air Combat Decision-Making Method Based on Game Tree and Digital Parallel Simulation Battlefield |
| ZHOU Wenjie1， FU Yulong1， GUO Xiangke2， QI Yutao1， ZHANG Haibin1 |
| 1. School of Cyber Engineering， Xidian University， Xi'an 710126， Shaanxi， China；
2. Air and Missile Defense College， Air Force Engineering University， Xi'an 710051， Shaanxi， China |
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Abstract In air combat decision-making, effectively identifying the key states and improving the decision-making ability of intelligent bodies in these states is the key research direction of reinforcement learning algorithms. In this paper, a dynamic strategy switching framework built by deep reinforcement learning was proposed for the intelligent body problem in air combat decision-making, aiming at increasing the decision-making quality of the intelligent body in the complex environment. This study identified critical states using dimensionality reduction and classification of high-dimensional state space through representation learning and cluster analysis techniques in the non-critical state, a deep reinforcement learning algorithm was employed for decision-making; in the critical state, an inverse dynamics model was adopted to generates the target state's corresponding action sequence and a parallel simulation strategy was utilized to execute the action sequence in multiple simulation environments to approximate the target state rapidly. At the end of the simulation, the optimal decision path was determined by advantage value evaluation. The experimental results show that the method can improve the decision-making ability of the intelligent body in critical states, providing a new solution for intelligent decision-making in complex air combat environments.
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Received: 29 January 2025
Published: 15 July 2025
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2025, Vol. 8 
