Reinforcement learning course at Data Science Retreat
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readme.md

Materials for the reinforcement learning course at Data Science Retreat.

This course is aimed at students with a grasp of supervised learning - no prior understanding of reinforcement learning required.

The course materials are:

This repo also has machine learning and reinforcement learning literature. Further resources (video lectures, blog posts etc) are listed in dsr-rl/resources.md.

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

Content

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

Goals for the course

Introduction to concepts, ideas and terminology. Familiarity with important literature. Understanding of the state of the field today. Practical strategies to run reinforcement learning experiments.

Where to go next

Further resources (video lectures, blog posts etc) are listed in dsr-rl/resources.md.