Introduction to Q-learning
Introduction to Q-learning
Released 10/2025
By Marc Harb
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 59m | Size: 127 MB
Released 10/2025
By Marc Harb
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 59m | Size: 127 MB
Q-learning is a powerful reinforcement learning (RL) algorithm for decision-making tasks. This course will teach you how to implement both tabular and deep Q-learning to train agents that learn optimal behaviors from their environment.
What you'll learn
Many aspiring machine learning engineers struggle to move from understanding reinforcement learning theory to applying it in code, especially when scaling to complex problems. In this course, Introduction to Q-learning, you’ll learn to implement both traditional and deep Q-learning to train intelligent agents. First, you’ll explore the foundations of Q-learning, its differences from other RL methods like SARSA, and the role of Q-functions and Q-tables. Next, you’ll discover how to build a deep Q-network that approximates Q-values using neural networks and updates using gradient descent. Finally, you’ll learn how to train your Q-network in Gym environments, use experience replay and target networks, and monitor learning over time. When you’re finished with this course, you’ll have the skills and knowledge of Q-learning needed to build scalable agents that learn from experience.