Deep Reinforcement Learning-Driven Multi-Agent Collaborative Optimization for Battlefield Kill Chain Efficiency

With the development of modern military technology, the application of kill chains has expanded significantly, covering a wide range of operational fields from land, sea, and air warfare to cyber warfare. In land warfare scenarios, due to complex terrain, rapid environmental changes, and fragmented battlefield information, achieving dynamic cooperation and real-time updates of the kill chain through multi-agent coalitions presents a key challenge in improving operational effectiveness. This paper models the construction of the land warfare kill chain as a multi-agent coalition construction problem. To address the challenges of low decision-making efficiency and unstable cooperation within coalitions, this paper proposes a coalition update mechanism based on game theory and mechanism design. The proposed mechanism integrates reinforcement learning techniques to optimize dynamic game behaviors among agents, thereby enhancing cooperative efficiency in complex adversarial environments. The innovation of this work lies in introducing an adaptive mechanism design that dynamically adjusts coalition members' decision rules according to changes in battlefield conditions, ensuring flexibility and robustness in the coalition. Experimental results demonstrate that this method significantly improves coalition operational effectiveness and collaboration in various combat scenarios, confirming its effectiveness and providing theoretical support and technical guidance for the construction of agent coalitions in land warfare systems.