Research on intelligent tactical decision making method of beyond visual range air combat based on DL MCTS

DOI:	10.11809/bqzbgcxb2025.02.020
Keywords:	beyond visual range; autonomous air combat; tactical decision making; intelligent decision making; deep learning; Monte Carlo tree search
Abstract:	Existing intelligent decision making research in beyond visual range (BVR) air combat mostly focuses on maneuvering decision making, while there is less research on tactical decision making. To address the issues of difficult maneuvering decision making comprehension and challenging tactical decision making generation, an algorithm integrating deep learning (DL) and Monte Carlo Tree Search (MCTS) is proposed. By constructing an autonomous learning and decision making framework for air combat agents, integrating the agents’ offline tactical learning and online tactical decision making, a BVR air combat tactical decision making method based on DL MCTS is realized. In the offline stage, historical engagement data and tactical theoretical knowledge are used to build a tactical database, including perception data sets, decision making data sets, and evaluation data sets. Moreover, three functional modules of perceptron, planner and evaluator for the agent is constructed and trained with deep neural networks based on the data base. In the real time confrontation stage, two parallel modes are designed for perception and decision making timeline independently. The Monte Carlo search method is introduced to fuse the three networks of the agent to realize selection, expansion, simulation and information backtracking at each node. The optimal strategies are searched and updated with DL MCTS in real time. Finally, experiments show that the agent after offline training has basic decision making capabilities. In a head on attack mission, after 50 cycles iterative search, the agent can eliminate the adversary’s first missile advantage and gradually acquire its own missile launch conditions. The experimental results demonstrate that the decision making outcomes of this tactical decision making method exhibit strong interpretability, and the decision making speed is satisfactory.
Issue:	Vol. 46 No. 2 (2025)
Published:	2025-02-28
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