Dynamic Pricing with Reinforcement Learning from Scratch: Q-Learning | by Nicolo Cosimo Albanese | Aug, 2023


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.
  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

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.

2.1 Key concepts

Reinforcement Learning (RL) is an area of Machine Learning where an agent learns to accomplish a task by trial and error.

In brief, the agent tries actions which are associated to a positive or negative feedback through a reward mechanism. The agent adjusts its behavior to maximize a reward, thus learning the best course of action to achieve the final goal.

Let us introduce the key concepts of RL through a practical example. Imagine a simplified arcade game, where a cat should navigate a maze to collect treasures — a glass of milk and a ball of yarn — while avoiding construction sites:

Image by author.
  1. The agent is the one choosing the course of actions. In the example, the agent is the player who controls the joystick deciding the next move of the cat.
  2. The environment is the…



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