A multiobjective reinforcement learning approach for AGV task clustering

The complete scheduling problem of unmanned warehouse AGV is a complex NP-hard problem with a process that includes three parts: task assignment dispatching, path planning, and traffic coordination. This study focuses on determining the optimal task assignment scheme for the AGV considering all known information. With the goal of balancing the workload at each picking station, an unsupervised reinforcement learning-based classification assignment method is proposed. The complex multi-task assignment problem is decomposed into a two-stage assignment problem. In order to reduce the task load of AGV and picking stations, the picking and storing region is first classified, and then tasks are assigned. The algorithm uses a hierarchical reinforcement learning method based on policy gradient to assign the storage nodes by class with the set optimization target. Experiments show that using this approach reduces the difficulty of the AGV scheduling problem and increases the efficiency of the solution.