Materials for the introductory reinforcement learning course A Glance at Reinforcement Learning. If you like this, Make sure you check out rl-resources, which contains all the literature used in the course and tips on where to go next.

This project is built and maintained by Adam Green - adam.green@adgefficiency.com.

The course is built using reveal.js. A pdf of the slides is included, or you can locally deploy the presentation using:

brew install node 

npm install

npm start

Goals for the course

The goals of the course are to:

• introduce you to the concepts, ideas and terminology of reinforcement learning
• become familiar with important literature
• understand the current state of the art
• pass on advice from running reinforcement learning experiments

Course content:

• background statistical concepts
• Markov Decision processes
• value function methods (DQN and it's extensions)
• policy gradient methods
• AlphaGo to AlphaZero
• practical advice for experiments
• current state of the art