This chapter introduces the concept of reinforcement learning, where the machine learning model learns by interacting with an environment and receiving feedback in the form of rewards or penalties. Students will understand the key components of reinforcement learning, such as the agent, environment, and reward function, and explore algorithms like Q-learning and policy gradients. They will also learn about the applications of reinforcement learning in areas like game AI, robotics, and resource optimization.