RetailNet: Enhancing Retails of Perishable Products with Multiple Selling Strategies via Pair-Wise Multi-Q Learning

We propose RetailNet, an end-to-end reinforcement learning (RL)-based neural network, to achieve efficient selling strategies for perishable products in order to maximize retailers’ long-term profit. We design pair-wise multi-Q network for Q value estimation to model each state-action pair and to capture the interdependence between actions. Generalized Advantage Estimation (GAE) and Entropy are incorporated into the loss function for balancing the tradeoff between exploitation and exploration. Experiments show that RetailNet efficiently produces the near-optimal solution, providing practitioners valuable guidance on their inventory replenishment, pricing, and products display strategies in the retailing industry.

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Recommended citation: Ma, Xiyao, Fan Lu, Xiajun Amy Pan, Yanlin Zhou, and Xiaolin Andy Li. “RetailNet: Enhancing Retails of Perishable Products with Multiple Selling Strategies via Pair-Wise Multi-Q Learning.” (2019).