Fundamentals of Deep Reinforcement Learning

Proficiency with Python

Functions, classes, objects, loops

Basic familiarity with Jupyter notebooks

Basic probability

Sampling from a normal distributon Conditional probability notation \mathbb{E}E - expectation \SigmaΣ - the summation operator

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.