About this course

This course starts from the very beginnings of Reinforcement Learning and works its way up to a complete understanding of Q-learning, one of the core reinforcement learning algorithms.
In part II of this course, you’ll use neural networks to implement Q-learning to produce powerful and effective learning agents (neural nets are the “Deep” in “Deep Reinforcement Learning”).

At a Glance:
Institution: LVx
Subject: Computer Science
Level: Introductory
Prerequisites:
Requirements:
Proficiency with Python
Functions, classes, objects, loops
Basic familiarity with Jupyter notebooks
Recommended Prerequisites:
Basic probability
Sampling from a normal distributon
Conditional probability notation
mathbb{E}E – expectation
SigmaΣ – the summation operator
Language: English
Video Transcript: English
Associated skills:Reinforcement Learning, Artificial Neural Networks, Q Learning, Deep Learning, Python (Programming Language)

What You’ll Learn:
About this course

This course starts from the very beginnings of Reinforcement Learning and works its way up to a complete understanding of Q-learning, one of the core reinforcement learning algorithms.
In part II of this course, you’ll use neural networks to implement Q-learning to produce powerful and effective learning agents (neural nets are the “Deep” in “Deep Reinforcement Learning”).