面向智能空战有人/无人机协同可解释方法OA

Manned/Unmanned Aerial Vehicle(M/UAV)teaming represents a critical operational paradigm for future air combat,where deep reinforcement learning serves as a key enabling technology.However,the"black-box na-ture"of deep reinforcement learning renders the learned strategies difficult to interpret and trust,making interpretable deep reinforcement learning essential for achieving intelligent air combat collaboration.This paper proposes a deep re-inforcement learning interpretation method based on the Bayesian Shapley framework,realizes the interpretability modeling and verification analysis of the decision-making process,and achieves the goal of explaining the decision-making basis of UAV.The proposed approach first constructs a decision intent analysis framework for cooperative mis-sions using dynamic Bayesian networks,capable of identifying critical decision nodes in trajectory segments.Subse-quently,the Shapley value-based contribution assessment algorithm is employed to achieve state-level quantitative analysis of decision rationale at key nodes.Finally,by reconstructing the state input space of the deep reinforcement learning model,the method significantly enhances model interpretability and trustworthiness while maintaining original policy performance,with the effectiveness of the explanatory results validated through state space ablation simulations.