Dynamic pricing and inventory control of perishable products by a deep reinforcement learning algorithm

Using continuous action spaces to set prices simultaneously and order quantities, this study proposes a unified deep reinforcement learning (DRL) framework for dynamic pricing and perishables inventory control in a vendor-managed environment. Sales revenue plus penalties for spoiling, returns, and transport costs are combined to create a multi-component reward that reflects profit. We incorporate a potential-based shaping term $\Phi \left(s\right)$ constructed from inventory heuristics to direct exploration and shorten training time, guaranteeing no change in policy optimality. In contrast to other DRL algorithms and classical benchmarks, our empirical study, which includes seasonal demand and random returns, shows that an agent based on proximal policy optimization achieves better cumulative reward and service level.