Optimization of Artificial Intelligence Network Resource Allocation by Reinforcement Learning Algorithm

With the rapid development of network technology, the problem of network resource allocation is increasingly prominent. The purpose of this study is to explore a more intelligent and efficient network resource allocation strategy by strengthening learning algorithms. In order to achieve this goal, this article first constructs a Deep Reinforcement Learning (DRL) model for network resource allocation, and uses Actor-Critic algorithm to optimize it. In the experiment, a simulated network environment is built, and the validity of the model is verified by preparing relevant data sets. During the experiment, the state, action, reward and other information of the algorithm are recorded in detail, so as to evaluate the performance comprehensively. The results show that DRL algorithm performs well in network resource allocation and significantly improves the overall performance of the network. Compared with traditional methods, DRL algorithm shows its powerful learning and optimization ability. The convergence and stability analysis of the algorithm further confirmed the reliability of DRL in network resource allocation. In this article, the application potential of DRL in network resource allocation is verified by experiments, which provides a new idea for solving the resource allocation problem in dynamic network environment.