Latest Courses
Angular 2 Master Class with Alejandro RangelCheck course
The Comple JavaScript From Beginner To AdvancedCheck course
The Complete Android Oreo and Nougat App TutorialsCheck course
C Programming Skills Test With ExplanationCheck course
Modern .NET Ecosystem and .NET CoreCheck course
Python Programming Bible: Hands-On Python 3 with 10 ProjectsCheck course
Introduction to Cloud Computing on Amazon AWS for BeginnersCheck course
Learn basics of Redux in React Native in 2 hours!Check course
Learn Python Django From ScratchCheck course
The Complete PHP Bootcamp Course With Video Sharing ProjectCheck course
Angular 2 Master Class with Alejandro RangelCheck course
The Comple JavaScript From Beginner To AdvancedCheck course
The Complete Android Oreo and Nougat App TutorialsCheck course
C Programming Skills Test With ExplanationCheck course
Modern .NET Ecosystem and .NET CoreCheck course
- 86% Modern Reinforcement Learning: Deep Q Learning in PyTorch

Modern Reinforcement Learning: Deep Q Learning in PyTorch

$114.99 $15.99Track price

Add your review
Add to wishlistAdded to wishlistRemoved from wishlist 0
Add to compare
8.2/10 (Our Score)
Product is rated as #206 in category Machine Learning

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.

Instructor Details

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.

Specification: Modern Reinforcement Learning: Deep Q Learning in PyTorch

Duration

5.5 hours

Year

2020

Level

Intermediate

Certificate

Yes

Quizzes

No

14 reviews for Modern Reinforcement Learning: Deep Q Learning in PyTorch

4.4 out of 5
9
4
1
0
0
Write a review
Show all Most Helpful Highest Rating Lowest Rating
  1. Avatar

    Fredrik Omstedt

    I took this course as preparation for writing my Master’s thesis. I think it was very good in that it dealt with understanding papers, something I definitely will have to do for my thesis. It also gave me a lot of insight on how to tackle a bunch of problems that might arise when doing my own implementations.

    Helpful(0) Unhelpful(0)You have already voted this
  2. Avatar

    Wong Yuh Sheng Reuben

    Phil is an amazing teacher and explains his code, and the ideas behind the implementation well.

    Helpful(0) Unhelpful(0)You have already voted this
  3. Avatar

    Charlotte Dejonckheere

    very clear

    Helpful(0) Unhelpful(0)You have already voted this
  4. Avatar

    Peter Blonner

    Very clear explanations and course mix of theory and practice suits my learning style

    Helpful(0) Unhelpful(0)You have already voted this
  5. Avatar

    Alexandru Jeman

    It is pretty good and straight to the point, above expectation if your are comparing to everything you see around. Code, classes and parameters are well explained, will save you weeks or months of learning.

    Helpful(0) Unhelpful(0)You have already voted this
  6. Avatar

    Robert Edgerton

    Yes. I am a life long learner and this subject intriques me.

    Helpful(0) Unhelpful(0)You have already voted this
  7. Avatar

    Danny

    Some of the things I really liked on this course are paper to code conversion, best, structured and reusable coding practices used throughout the course. Some times I felt like he was going bit faster than my ability to understand new things but of course I can always hit pause or play the video slower.

    Helpful(0) Unhelpful(0)You have already voted this
  8. Avatar

    Robert Wilkerson

    The box classification example is not the best application of RL. Could have picked a better example.

    Helpful(0) Unhelpful(0)You have already voted this
  9. Avatar

    CARLOMARTI149 .

    Perfect. CS Major and C/C++/ some python and want more

    Helpful(0) Unhelpful(0)You have already voted this
  10. Avatar

    Monica Fd

    Amazing course and professor! Really love his style of teaching. I am doing my masters in ML and I am supplementing my courses with his teaching.

    Helpful(0) Unhelpful(0)You have already voted this
  11. Avatar

    Adithya T P

    Really like the coding videos. Hard to come by videos of this quality.

    Helpful(0) Unhelpful(0)You have already voted this
  12. Avatar

    Jason Achonu

    Perfect. Great way to get into the habit of reading papers and implementing them.

    Helpful(0) Unhelpful(0)You have already voted this
  13. Avatar

    Victor Andrean

    I am very very satisfied. Good job Dr. Phil. You make the course concise but still comprehensive. In addition, the part of consolidating all the code. That’s very smart. Thank you so much

    Helpful(0) Unhelpful(0)You have already voted this
  14. Avatar

    AnthonyL

    My hopes; accomplish next level conceptualization. Phil’s approach is shear brilliance.

    Helpful(0) Unhelpful(0)You have already voted this

    Add a review

    Your email address will not be published. Required fields are marked *

    This site uses Akismet to reduce spam. Learn how your comment data is processed.

    Modern Reinforcement Learning: Deep Q Learning in PyTorch
    Modern Reinforcement Learning: Deep Q Learning in PyTorch

    $114.99 $15.99

    Price tracking

    Register New Account
    Reset Password
    Compare items
    • Total (0)
    Compare