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Modern Reinforcement Learning: Deep Q Learning in PyTorch

 

Modern Reinforcement Learning: Deep Q Learning in PyTorch

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How to Turn Deep Reinforcement Learning Research Papers Into Agents That Beat Classic Atari Games

What you'll learn

  • How to read and implement deep reinforcement learning papers
  • How to code Deep Q learning agents
  • How to Code Double Deep Q Learning Agents
  • How to Code Dueling Deep Q and Dueling Double Deep Q Learning Agents
  • How to write modular and extensible deep reinforcement learning software
  • How to automate hyperparameter tuning with command line arguments

Course content
9 sections • 41 lectures • 5h 41m total length

Requirements

  • Some College Calculus
  • Exposure To Deep Learning
  • Comfortable with Python

Description

Machine Learning and Deep Learning Bootcamp in Python

In this complete deep reinforcement learning course you will learn a repeatable framework for reading and implementing deep reinforcement learning research papers. You will read the original papers that introduced the Deep Q learning, Double Deep Q learning, and Dueling Deep Q learning algorithms. You will then learn how to implement these in pythonic and concise PyTorch code, that can be extended to include any future deep Q learning algorithms. These algorithms will be used to solve a variety of environments from the Open AI gym's Atari library, including Pong, Breakout, and Bankheist.

You will learn the key to making these Deep Q Learning algorithms work, which is how to modify the Open AI Gym's Atari library to meet the specifications of the original Deep Q Learning papers. You will learn how to:

  • Repeat actions to reduce computational overhead
  • Rescale the Atari screen images to increase efficiency
  • Stack frames to give the Deep Q agent a sense of motion
  • Evaluate the Deep Q agent's performance with random no-ops to deal with model over training
  • Clip rewards to enable the Deep Q learning agent to generalize across Atari games with different score scales

If you do not have prior experience in reinforcement or deep reinforcement learning, that's no problem. Included in the course is a complete and concise course on the fundamentals of reinforcement learning. The introductory course in reinforcement learning will be taught in the context of solving the Frozen Lake environment from the Open AI Gym. 

Advanced AI: Deep Reinforcement Learning in Python

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