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

Mohit Apte, Ketan Kale, Pranav Datar, Pratiksha Deshmukh

TL;DR

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.

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.

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

TL;DR

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.

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.

Paper Structure

This paper contains 17 sections, 3 equations, 1 figure, 3 tables.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Traditional Operations Research Approaches
  4. Challenges in Traditional Methods
  5. Introduction to Reinforcement Learning in Pricing
  6. Applications of RL in Dynamic Pricing
  7. Methodology
  8. Simulation Environment Setup
  9. Model Parameters
  10. Q-Learning Algorithm
  11. Actions and State Space
  12. Reward Structure
  13. Implementation Steps
  14. Dataset Description
  15. Observations
  16. ...and 2 more sections

Figures (1)

  • Figure 1: Revenue Curve from the Demand Function. The figure illustrates how revenue varies with changes in price, highlighting optimal pricing points that maximize revenue based on demand elasticity.