Dynamic Pricing with Reinforcement Learning from Scratch: Q-Learning

An introduction to Q-Learning with a practical Python example

Exploring prices to find the optimal action-state values to maximize profit. Image by author.

Table of contents

1. Introduction
2. A primer on Reinforcement Learning
   2.1 Key concepts
   2.2 Q-function
   2.3 Q-value
   2.4 Q-Learning
   2.5 The Bellman equation
   2.6 Exploration vs. exploitation
   2.7 Q-Table
3. The Dynamic Pricing problem
   3.1 Problem statement
   3.2 Implementation
4. Conclusions
5. References

1. Introduction

In this post, we introduce the core concepts of Reinforcement Learning and dive into Q-Learning, an approach that empowers intelligent agents to learn optimal policies by making informed decisions based on rewards and experiences.

We also share a practical Python example built from the ground up. In particular, we train an agent to master the art of pricing, a crucial aspect of business, so that it can learn how to maximize profit.

Without further ado, let us begin our journey.

2. A primer on Reinforcement Learning

2.1 Key concepts