SIMULATION ANALYSIS OF A DEEP REINFORCEMENT LEARNING APPROACH FOR TASK SELECTION BY AUTONOMOUS MATERIAL HANDLING VEHICLES

The use of autonomous vehicles is a growing trend in the material handling and warehousing. Some challenges that face material handling include the navigation within a warehouse, precision localization and movement, and task selection decisions. In this paper, we address the issue of task selection. In particular, we develop a deep reinforcement learning methodology to enable a vehicle to select from among multiple tasks and move to the closest task in the context of material handling in a warehouse. To evaluate the deep reinforcement learning methodology, we conduct a simulation-based experiment to generate scenarios to first train and then test the capabilities of the method. The results of the experiment show that the method performs well under the given conditions.