Explainability-driven LangChain-integrated large language models and knowledge graphs for multiagent reinforcement learning in complex air combat simulation
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 scenario...
Abderahim Salhi, Indu Shukla, Gary J. Briggs et al.
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