Research on key technologies of multi-agent game decision distributed learning system for agent equipment decision parameter optimization

The optimization of decision parameters for equipment in the Intelligent agent domain is becoming increasingly paramount, driven by advancements in modern technology and the intricacies of battlefield environments. Multi-agent game theory, by simulating the interactions and confrontations of various agents under diverse scenarios, can authentically replicate the complex dynamic relationships inherent in the battlefield. Meanwhile, distributed learning systems demonstrate unique advantages in managing vast data sets and intricate computations. The synergy of these two approaches not only enhances the accuracy and timeliness of decision-making but also propels the innovation and advancement of intelligent agent technology. The transition from theory to practical application encompasses numerous critical technologies, including the construction of multi-agent game models, algorithm design, and the establishment of distributed architectures, all aimed at achieving efficient and precise optimization of intelligent agent equipment decision parameters.