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30-Day Money-Back Guarantee

This course includes:

  • 5.5 hours on-demand video
  • 24 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
Development Data Science Deep Learning

Modern Reinforcement Learning: Deep Q Learning in PyTorch

How to Turn Deep Reinforcement Learning Research Papers Into Agents That Beat Classic Atari Games
Rating: 4.6 out of 54.6 (409 ratings)
1,873 students
Created by Phil Tabor
Last updated 10/2020
English
English [Auto]
30-Day Money-Back Guarantee

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

  • Preview04:07
  • Preview03:46
  • Preview04:45

  • Agents, Environments, and Actions
    10:04
  • Markov Decision Processes
    11:30
  • Value Functions, Action Value Functions, and the Bellman Equation
    08:10
  • Model Free vs. Model Based Learning
    03:34
  • The Explore-Exploit Dilemma
    05:27
  • Temporal Difference Learning
    22:01

  • Dealing with Continuous State Spaces with Deep Neural Networks
    18:53
  • Naive Deep Q Learning in Code: Step 1 - Coding the Deep Q Network
    07:55
  • Naive Deep Q Learning in Code: Step 2 - Coding the Agent Class
    10:10
  • Naive Deep Q Learning in Code: Step 3 - Coding the Main Loop and Learning
    09:21
  • Preview02:13
  • Preview02:42
  • Dealing with Screen Images with Convolutional Neural Networks
    03:52

  • How to Read Deep Learning Papers
    07:15
  • Analyzing the Paper
    20:33
  • How to Modify the OpenAI Gym Atari Environments
    14:29
  • How to Preprocess the OpenAI Gym Atari Screen Images
    02:55
  • How to Stack the Preprocessed Atari Screen Images
    03:26
  • How to Combine All the Changes
    01:30
  • How to Add Reward Clipping, Fire First, and No Ops
    04:49
  • How to Code the Agent's Memory
    10:55
  • How to Code the Deep Q Network
    11:44
  • Coding the Deep Q Agent: Step 1 - Coding the Constructor
    07:48
  • Coding the Deep Q Agent: Step 2 - Epsilon-Greedy Action Selection
    02:22
  • Coding the Deep Q Agent: Step 3 - Memory, Model Saving and Network Copying
    04:24
  • Coding the Deep Q Agent: Step 4 - The Agent's Learn Function
    07:54
  • Coding the Deep Q Agent: Step 5 - The Main Loop and Analyzing the Performance
    14:14

  • Analyzing the Paper
    15:39
  • Coding the Double Q Learning Agent and Analyzing Performance
    08:51

  • Analyzing the Paper
    14:01
  • Coding the Dueling Deep Q Network
    03:21
  • Coding the Dueling Deep Q Learning Agent and Analyzing Performance
    10:10
  • Coding the Dueling Double Deep Q Learning Agent and Analyzing Performance
    05:36

  • Implementing a Command Line Interface for Rapid Model Testing
    09:30
  • Consolidating Our Code Base for Maximum Extensability
    18:32
  • How to Test Our Agent and Watch it Play the Game in Real Time
    07:39

  • Preview04:40

  • Bonus Video: Where to Go From Here
    01:12

Requirements

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

Description

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.

We will cover:

  • Markov decision processes

  • Temporal difference learning

  • The original Q learning algorithm

  • How to solve the Bellman equation

  • Value functions and action value functions

  • Model free vs. model based reinforcement learning

  • Solutions to the explore-exploit dilemma, including optimistic initial values and epsilon-greedy action selection

Also included is a mini course in deep learning using the PyTorch framework. This is geared for students who are familiar with the basic concepts of deep learning, but not the specifics, or those who are comfortable with deep learning in another framework, such as Tensorflow or Keras. You will learn how to code a deep neural network in Pytorch as well as how convolutional neural networks function. This will be put to use in implementing a naive Deep Q learning agent to solve the Cartpole problem from the Open AI gym. 

Who this course is for:

  • Python developers eager to learn about cutting edge deep reinforcement learning

Instructor

Phil Tabor
Machine Learning Engineer
Phil Tabor
  • 4.5 Instructor Rating
  • 513 Reviews
  • 2,122 Students
  • 2 Courses

In 2012 I received my PhD in experimental condensed matter physics from West Virginia University. Following that I was a dry etch process engineer for Intel Corporation, where I leveraged big data to make essential process improvements for mission critical products. After leaving Intel in 2015, I have worked as a contract and freelance deep learning and artificial intelligence engineer.

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