Research on Intelligent Adaptive Optimization Model System of Computing System Based on Reinforcement Learning

This paper achieves the dynamic optimization of system performance through the application of Q-learning and deep reinforcement learning (DQN). In traditional systems, decision-making mechanisms often lack self-adjusting capabilities for various requirements. By using adaptive optimization techniques, the system can make real-time adjustments according to the feedback from the operating environment, thus optimizing the decision-making process. The system is mainly composed of two parts: algorithm design and model simulation. Simulation results indicate that in multiple test scenarios, this system significantly enhances the operational efficiency and performance. Data analysis reveals that compared with traditional static mechanisms, the system's task completion rate has increased by 15% and the operation time has been reduced by 12 %. Moreover, the universality of the adaptive optimization mechanism in different operating scenarios has been verified through precise analysis of simulation data. In conclusion, this study demonstrates that the adaptive system optimization based on reinforcement learning can not only remarkably improve the performance of individualized decision-making, but also provide effective theoretical support and technical reference for the design of future intelligent systems.