Explainability-driven LangChain-integrated large language models and knowledge graphs for multiagent reinforcement learning in complex air combat simulation

<jats:p>This work advances multiagent reinforcement learning (MARL) for complex air combat by integrating methods that enhance decision-making and explainability. Using a realistic six-degrees-of-freedom aerial simulation built on the OpenAI Gymnasium framework, we investigate competitive agent interactions in dynamic scenarios. We apply explainability techniques to clarify agent behavior and interaction patterns. The MARL framework is further augmented with knowledge graphs, large language models, and modular orchestration using the LangChain framework. This combines data-driven learning with knowledge-driven reasoning to strengthen situational awareness, coordination, and interpretability. Experimental results indicate that this integration sustains competitive performance while enhancing transparency and human interpretability.</jats:p>