Dynamic Pricing with Reinforcement Learning from Scratch: Q-Learning

<h1>1. Introduction</h1> <p>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.</p> <p>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.</p> <p>Without further ado, let us begin our journey.</p> <h1>2. A primer on Reinforcement Learning</h1> <h2>2.1 Key concepts</h2> <p>Reinforcement Learning (RL) is an area of Machine Learning where an agent learns to accomplish a task by trial and error.</p> <p>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.</p> <p>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 &mdash; a glass of milk and a ball of yarn &mdash; while avoiding construction sites:</p> <p><a href="https://towardsdatascience.com/dynamic-pricing-with-reinforcement-learning-from-scratch-q-learning-fb3fb764da49"><strong>Learn More</strong></a></p>