In this introductory hands-on lab on Reinforcement Learning (RL), participants will dive into the fundamental concepts of RL, including the exploration of key terms such as agents, environments, states, actions, and rewards. 1. Understand the basic principles and terminology of Reinforcement Learning. 2. Learn how to set up and define an RL problem, including the formulation of states, actions, and rewards. 3. Gain practical experience with simple RL algorithms through hands-on coding exercises. 4. Explore real-world applications of RL to contextualize theoretical concepts.