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.
Overview
Syllabus
- Unit 1: Introduction to Q-Learning: Building Intelligent Agents
- Q-Learning Update Function
- Fix the Q-Learning Update Function
- Handling Terminal States in Q-Learning
- Calculating Temporal Difference Error
- Simulate Q-Learning!
- Unit 2: Training a Q-Learning Agent in a Line-World Environment
- Q-Learning Update in Action
- Starting from the Edges
- Fix the Line-World Boundaries
- Navigating the Line World
- Building a Q-Learning Agent
- Unit 3: Using Q-Tables for Decision Making and Encapsulation in Q-Learning
- Fix the Q-Learning Agent Bug
- Implementing the Act Method
- Enhancing Q-Learning Decision Making
- Extracting the policy from the Q-table
- Training a Q-Learning Agent
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Learning Agents Using Q-Learning: Theory and Code - Agentic AI Course Lecture 13
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Reinforcement Learning 101
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