Fundamentals of Deep Reinforcement Learning

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").

• The theoretical underpinnings of Reinforcement Learning ("RL").
• How to implement each piece of theory to solve real problems in Python.
• The core RL formula: The Bellman Equation
• The Q-Learning algorithm, as well as many powerful improvements.
• Enough to prepare you for implement Reinforcement Learning algorithms using Deep Neural Networks (Part II).

• Proficiency with Python
• Functions, classes, objects, loops
• Basic familiarity with Jupyter notebooks
• Sampling from a normal distributon
• Conditional probability notation
• \mathbb{E}E - expectation
• \SigmaΣ - the summation operator