Research on intelligent combat decision making based on deep reinforcement learning

Yao Wang; Qijie Chen; Haiqiang Ma; Yi Jiang

doi:10.1117/12.3009215

16 October 2023 Research on intelligent combat decision making based on deep reinforcement learning

Yao Wang, Qijie Chen, Haiqiang Ma, Yi Jiang

Proceedings Volume 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023); 128032D (2023) https://doi.org/10.1117/12.3009215
Event: 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), 2023, Wuhan, China

Abstract

With the operational advantages of unmanned combat platforms in modern war gradually appearing, the research of unmanned combat platforms has become the focus of all circles. In order to realize intelligent and autonomous unmanned operation in a real sense, a combat mission computer based on AI development board was proposed to be built as the control core of unmanned vehicles, simulate the operational mobility situation diagram of unmanned vehicles, and use the deep reinforcement learning network DQN to establish angle and distance decision-making network, so as to realize intelligent mobility decision-making of unmanned vehicles. The experiment verified that the unmanned vehicle can maneuver to the target area autonomously, which proved that the deep reinforcement learning network can realize the feasibility of platform autonomous and intelligent decision-making, and provided a feasible technical approach and theoretical support for the construction of combat mission computer to realize intelligent, autonomous and unmanned combat in a real sense.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yao Wang, Qijie Chen, Haiqiang Ma, and Yi Jiang "Research on intelligent combat decision making based on deep reinforcement learning", Proc. SPIE 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023), 128032D (16 October 2023); https://doi.org/10.1117/12.3009215

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