Food waste is a major societal, environmental, and financial problem. One of the main actors are grocery stores. Policies for reducing food waste in those are complex due to a large number of uncertain heterogeneous factors like non-fully predictable demand.

Directly comparing food waste reduction policies through field experimentation is contrary to the very target of food waste reduction. This is why we propose RetaiL, a new simulation framework, to optimise grocery store restocking for waste reduction. RetaiL offers its users the possibility to create synthetic product data, based on real data from a European retailer. It then matches simulated customer demand to a restocking policy for those items, and evaluates a utility function based on generated waste, item availability to customers and sales. This allows RetaiL to function as a new Reinforcement Learning Task, where the agent has to act on restocking level given the state of the store, and receives this utility function as a reward.

In this demo, we let you open your own grocery store and manage its orders to the warehouse. Can you help in the fight against food waste?