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

@article{Li2018SIMULATIONAO,
  title={SIMULATION ANALYSIS OF A DEEP REINFORCEMENT LEARNING APPROACH FOR TASK SELECTION BY AUTONOMOUS MATERIAL HANDLING VEHICLES},
  author={Maojia Patrick Li and Prashant Sankaran and Michael E. Kuhl and Amlan Ganguly and Andres Kwasinski and Raymond W. Ptucha},
  journal={2018 Winter Simulation Conference (WSC)},
  year={2018},
  pages={1073-1083}
}
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… CONTINUE READING

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