Adaptive Resource Allocation and Service Optimization in the Technical Platform Based on Reinforcement Learning

In response to the problems of low resource utilization and difficult service optimization in the resource allocation of the technology center, this article adopts reinforcement learning to optimize the adaptive resource allocation and services of the technology center, aiming to improve resource management and service response capabilities through reinforcement learning algorithms and integrated architecture management. Reinforcement learning algorithms establish predictive models based on historical task data and current load conditions to monitor the resource requirements of various services in real-time. Through the feedback mechanism of state action pairs, the algorithm can continuously optimize resource allocation strategies to ensure priority allocation to high priority tasks when resources are scarce. At the same time, reinforcement learning can also predict the resource demand of sudden tasks, adjust the allocation of computing resources in advance, and reduce task queuing and system bottlenecks. The results showed that with the introduction of reinforcement learning strategy, the resource utilization rate decreased from 85% to 80% in case of faults, and the task success rate also decreased from 98% to 96%. However, the overall performance decline was relatively limited, indicating that the system can quickly recover most of its performance after recovering from faults and continue to maintain high service quality. The integration optimization strategy based on reinforcement learning and technology platform effectively solves the shortcomings of traditional resource management mode and provides strong technical support for enterprise digital transformation.