People’s livelihood demand prediction based on improved generative adversarial networks and reinforcement learning

At present, people's livelihood demand data collection is often affected by factors such as limited geographical coverage, a high proportion of sudden demands, and inconsistent data recording periods. The scarcity of small samples and non-stationary distribution are common. To this end, this study proposes a people's livelihood demand prediction strategy that integrates improved deep convolutional generative adversarial networks and reinforcement learning. Firstly, the convolutional layers and batch normalization structure of the adversarial network are generated through deep convolution. Secondly, a reinforcement learning architecture is constructed, in which the problem representation module adopts a bidirectional long short-term memory network combined with an attention mechanism. The results show that the model has the best prediction accuracy for long-term trends, and the symmetric mean absolute percentage errors are less than 10%. The short-term root mean square error of dataset A is less than 1.05. In the response test, the 30-3 window is optimal for traffic type, and the 30-7 window is optimal for industrial noise in the long term. The proposed model can effectively improve the robustness of demand prediction and provide technical support for the refined management of government affairs.