Q-Learning Unleashed: Building Intelligent Agents

Overview

In this course, we focus on building a Q-learning agent step by step. We start with the Bellman equation and the Q-table update, then implement a basic Q-learning function.

Next, we incorporate an exploration policy (ε-greedy), and finally we demonstrate how to use the learned Q-table for decision making.

Syllabus

• Lesson 1: Introduction to Q-Learning: Building Intelligent Agents
• Lesson 2: Training a Q-Learning Agent in a Line-World Environment
• Lesson 3: Using Q-Tables for Decision Making and Encapsulation in Q-Learning

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Subjects

Computer Science