A Deep Reinforcement Learning Model for a Two-Layer Scheduling Policy in Urban Public Resources

The issue of efficient scheduling and deployment of urban public resources has become increasingly important with the development of technological innovations and the mobility of societies. The arbitrary usage behavior of users causes the unbalanced distribution of resources and makes it difficult for users to get adequate resources in some places but redundant resources in others. Therefore, designing an efficient scheduling policy for public resources becomes crucial to promoting resource utilization and customer satisfaction. In this article, we propose a novel scheduling system for public resources that aligns with the actual value-driven scheduling strategy and take the bike-sharing system as an example. Then, we design a deep reinforcement learning algorithm named two action layer proximal policy optimization (TALPPO) to generate an effective sharing-bike scheduling strategy under realistic constraints, which could help enterprises to make better management and operation decisions. Finally, we compare the proposed algorithm with the other ten baseline models and provide extensive experimental results on two data sets called Mobike (dockless) and Citi Bike (docked) to evaluate the performance of our proposed approach.