Cross-Domain Functional Knowledge Integration Architecture Powered by Deep Reinforcement Learning

To address the challenges associated with the inefficient generation of product functional design schemes in the face of complex user requirements, a multi-expert optimized reinforcement learning search network is proposed. By adding non-functional factors to functional design knowledge representation, the algorithm breaks through the limitations of traditional single-dimensional evaluations and optimizes the generated functional unit chains. A highly efficient circular experience pool and dynamic priority sampling strategy are proposed to improve experience storage efficiency and training stability. Combining the dynamic weighting mechanism and the Mixture of Experts Model enhances the algorithm’s adaptability to complex design tasks. Experiments show that the circular experience pool technology can eliminate memory fragmentation, increase storage efficiency by 86.60 , and accelerate model convergence speed by 88.20 . The dynamic weighting mechanism maintains a stable success rate of 93.60 in scenarios with variable requirements, and the MoE model increases the search success rate to 94.33 .