A Deep Q-Network Based on Radial Basis Functions for Multi-Echelon
  Inventory Management
Liqiang Cheng Jun Luo Weiwei Fan Yidong Zhang Yuan Li
This paper addresses a multi-echelon inventory management problem with a
complex network topology where deriving optimal ordering decisions is
difficult. Deep reinforcement learning (DRL) has recently shown potential in
solving such problems, while designing the neural networks in DRL remains a
challenge. In order to address this, a DRL model is developed whose Q-network
is based on radial basis functions. The approach can be more easily constructed
compared to classic DRL models based on neural networks, thus alleviating the
computational burden of hyperparameter tuning. Through a series of simulation
experiments, the superior performance of this approach is demonstrated compared
to the simple base-stock policy, producing a better policy in the multi-echelon
system and competitive performance in the serial system where the base-stock
policy is optimal. In addition, the approach outperforms current DRL
approaches.