Research and application of computer laboratory resource scheduling algorithm based on deep learning optimization

The resource scheduling of computer laboratory is facing diversified and dynamic user needs, and the traditional scheduling algorithm has limitations in complex environment. In this paper, a resource scheduling algorithm based on Deep Reinforcement Learning (DRL) is proposed, which uses Deep Q Network (DQN) as a model, and optimizes resource allocation through state space modeling, action space design and multi-objective reward function. The experimental scenario simulates the dynamic load environment and compares it with the traditional algorithm and baseline model. The results show that the proposed Dueling DQN algorithm improves the resource utilization rate, task completion rate and high-priority task completion rate by 7-18%, 10-20% and 8.2% respectively, and reduces the average waiting time by 30-50%. Through dynamic adaptability and multi-objective optimization, the algorithm maintains a high resource utilization rate during peak load period, which is significantly superior to the traditional methods.