Module 7

Reinforcement Learning
Welcome to Module 7 Reinforcement Learning, where we'll explore one of the most fascinating approaches in artificial intelligence. Imagine teaching a child to ride a bicycle - they learn through trial and error, falling down and getting back up, gradually improving with each attempt. This is exactly how Reinforcement Learning works in AI.
At its core, Reinforcement Learning is about learning through experience. Unlike traditional learning methods where an AI is given correct answers (supervised learning) or asked to find patterns (unsupervised learning), RL agents learn by interacting with their environment - much like humans do. They take actions, observe the consequences, and adjust their behavior based on the rewards or penalties they receive.
This powerful approach has led to remarkable breakthroughs in artificial intelligence. From teaching computers to master complex games like Chess and Go, to helping robots learn to walk and manipulate objects, to optimizing energy consumption in data centers - Reinforcement Learning is transforming how machines learn and adapt. 
As we explore this module, you'll discover how these principles can be applied to solve real-world challenges in ways that were once thought impossible.
Learning Objectives
Lesson 7.1: Understanding Reinforcement Learning Concepts

Gain a comprehensive understanding of Reinforcement Learning principles, including agents, environments, rewards, and policies.

Lesson 7.2: Markov Decision Processes and Q-learning

Explore foundational concepts such as Markov Decision Processes (MDPs) and Q-learning, essential frameworks for modeling and solving sequential decision-making problems.

Lesson 7.3: Applications in Gaming and Robotics

Learn about real-world applications of Reinforcement Learning in gaming and robotics, where agents learn optimal strategies through interaction with their environments.

Lesson 7.4: Hands-on Exercise: Implementing a Basic Reinforcement Learning Algorithm

Engage in a hands-on exercise where you will implement a basic Reinforcement Learning algorithm. Apply your knowledge to build and train an RL agent to solve a specific task or game.