基于多智能体强化学习的流程工业多操作参数协同优化OA

Process industries are often confronted with strong multi-operational parameter couplings,intricate pro-cess topologies,and difficulties in multi-stage coordination,which render conventional localized optimization meth-ods inadequate for achieving global optimality.To address these challenges,this paper proposes a graph spectral theory-based process topology-aware multi-agent reinforcement learning collaborative optimization method for mul-tiple operating parameter collaborative optimization in complex topological process industries.Specifically,a topo-logy analysis framework based on Laplacian spectral analysis is developed to characterize structural coupling rela-tionships among multiple operating parameters,thereby supporting agent task allocation and coordinated decision-making.Subsequently,a temporal perception module integrating long short-term memory networks with a multi-head attention mechanism is designed to extract key temporal dependencies from historical state trajectories.Fur-thermore,a hierarchical spatial attention mechanism is introduced to enable dynamic and adaptive regulation of op-timization attention across organizational,variable,and continuous control domains.On this basis,a hierarchical reinforcement learning architecture is constructed to coordinate local and global policy optimization,facilitating co-operative control and strategy optimization among multiple agents.Simulation experiments using industrial data from a continuous stirred tank reactor system and a representative salt-lake chemical process validate the effective-ness of the proposed method.Experimental results show that the proposed method achieves up to a 41.2%perform-ance improvement over conventional approaches,exhibiting superior convergence behavior and policy stability,and providing a viable technical pathway for multiple operating parameter collaborative optimization in process industries.