Dynamic Retail Pricing via Q-Learning -- A Reinforcement Learning Framework for Enhanced Revenue Management

TL;DR

This paper introduces a reinforcement learning framework using Q-Learning for dynamic retail pricing, enabling real-time adaptation to market changes and improving revenue over traditional static methods.

Contribution

It presents a novel RL-based approach for retail pricing that adapts dynamically to market conditions, outperforming traditional static pricing strategies.

Findings

RL approach surpasses traditional methods in revenue

Effective in adapting to consumer behavior changes

Provides insights into price elasticity and demand

Abstract

This paper explores the application of a reinforcement learning (RL) framework using the Q-Learning algorithm to enhance dynamic pricing strategies in the retail sector. Unlike traditional pricing methods, which often rely on static demand models, our RL approach continuously adapts to evolving market dynamics, offering a more flexible and responsive pricing strategy. By creating a simulated retail environment, we demonstrate how RL effectively addresses real-time changes in consumer behavior and market conditions, leading to improved revenue outcomes. Our results illustrate that the RL model not only surpasses traditional methods in terms of revenue generation but also provides insights into the complex interplay of price elasticity and consumer demand. This research underlines the significant potential of applying artificial intelligence in economic decision-making, paving the way for more sophisticated, data-driven pricing models in various commercial domains.