Reinforcement Learning for Retail Price Optimisation: State, Action, Reward Design
Reinforcement Learning for Retail Price Optimisation: State, Action, Reward Design
September 10, 2025 - Reading time: 5 minutes
UK retailers are exploring reinforcement learning to move past static rules and blunt markdown schedules. The aim is smarter price moves that respond to demand in near real time while protecting long-term margin and customer trust. In this article, we'll explore what to do, how to frame state, action, and reward, set sensible guardrails, and wire the outputs into day-to-day trading.
For a clear UK overview of what reinforcement learning involves, see The Alan Turing Institute's reinforcement learning research area, which explains how agents learn through interaction rather than from fixed datasets.
Getting results in practice depends on clean data and tight guardrails, and also on operational execution. Platforms such as Retail Express can publish approved prices and promotions across web and store so decisions made by models are applied consistently. That operational layer should exchange data with your retail assortment planning solution, so buy depth, markdown schedule, and price ladders all support the same strategy..