Machine Learning: Beginner Reinforcement Learning in Python

How to teach a neural network to play a game using delayed gratification in 146 lines of Python code

This course is designed for beginners to machine learning. Some of the most exciting advances in artificial intelligence have occurred by challenging neural networks to play games. I will introduce the concept of reinforcement learning, by teaching you to code a neural network in Python capable of delayed gratification.

What you’ll learn

  • Machine Learning.
  • Artificial Intelligence.
  • Neural Networks.
  • Reinforcement Learning.
  • Deep Q Learning.
  • OpenAI Gym.
  • Keras.
  • Tensorflow.
  • Bellman Equation.

Course Content

  • Introduction –> 4 lectures • 16min.
  • Creating your Agent and Environment –> 7 lectures • 33min.
  • Q Learning –> 5 lectures • 25min.
  • Neural Networks –> 3 lectures • 8min.
  • Deep Q Learning –> 5 lectures • 23min.

Machine Learning: Beginner Reinforcement Learning in Python

Requirements

  • Basic knowledge of Python.

This course is designed for beginners to machine learning. Some of the most exciting advances in artificial intelligence have occurred by challenging neural networks to play games. I will introduce the concept of reinforcement learning, by teaching you to code a neural network in Python capable of delayed gratification.

We will use the NChain game provided by the Open AI institute. The computer gets a small reward if it goes backwards, but if it learns to make short term sacrifices by persistently pressing forwards it can earn a much larger reward. Using this example I will teach you Deep Q Learning – a revolutionary technique invented by Google DeepMind to teach neural networks to play chess, Go and Atari.