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
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: