Modeling and optimization of computer laboratory automation management system based on reinforcement learning

This study focuses on intelligent and dynamic optimization of computer laboratory management, proposing a modeling and optimization framework for an automated management system integrated with reinforcement learning (RL). To address the limitations of traditional management approaches in resource allocation efficiency, energy consumption control, and user experience, the RL technique is introduced to construct an intelligent agent based on the multi-objective proximal policy optimization (MO-PPO) framework, enabling dynamic resource scheduling for heterogeneous laboratory equipment. The system comprehensively describes the dynamic environment of the laboratory through multi-dimensional feature vectors, designs a mixed action space to meet the requirements of equipment management, and constructs a multi-objective weighted reward function to comprehensively optimize efficiency, energy consumption and fairness. In the implementation of the algorithm, the dual-branch strategy network is used to deal with the mixed action space, the adaptive entropy regularization mechanism and the graph stack are introduced to improve the decision-making ability, and the hierarchical experience playback mechanism is designed to optimize the environmental interaction. The experimental results show that compared with static scheduling and genetic algorithm, this method has significantly improved the key indicators such as average task delay, energy consumption cost, standard deviation of user satisfaction and equipment overload events, and has excellent adaptability in dynamic environment. In the A/B test of a university laboratory, the new system effectively reduces the task delay and energy consumption, and significantly improves the user satisfaction, showing good application prospects and practical benefits.