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- 85% Modern Reinforcement Learning: Deep Q Learning in PyTorch

Modern Reinforcement Learning: Deep Q Learning in PyTorch

$109.99 $15.99Track price

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8.2/10 (Our Score)
Product is rated as #232 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

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

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  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.

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  2. Avatar

    Wong Yuh Sheng Reuben

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

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  3. Avatar

    Charlotte Dejonckheere

    very clear

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  4. Avatar

    Peter Blonner

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

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  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.

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  6. Avatar

    Robert Edgerton

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

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  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.

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  8. Avatar

    Robert Wilkerson

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

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  9. Avatar

    CARLOMARTI149 .

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

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  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.

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  11. Avatar

    Adithya T P

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

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  12. Avatar

    Jason Achonu

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

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  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

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  14. Avatar

    AnthonyL

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

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  15. Avatar

    Keshav Inamdar

    Yes. This has been of immense value to me. I was very comfortable with the maths but was not sure how exactly to implement it. I was trying to struggle my way through using Tensor Flow . But This course gave me a great push in learning on how to implement.

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  16. Avatar

    Piotr Ostrowski

    The course is very decent for a person with mathematical background, otherwise it may feel slightly abstract and overwhelming.

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  17. Avatar

    Azeruddin Sheikh Azeemuddin Sheikh

    Expert tutor. Love the instructions!

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  18. Abhijit Ghosh

    Abhijit Ghosh

    Yes the instructor is good and gives a very good hands on experience rather than theoretical only.

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  19. Avatar

    Sanjeev Dubey

    I love that instructor has explicitly mentioned that course has a good difficulty index and would present all tough parts as well (all the maths), rather than making it look like magic and teach nothing or fairly obvious things.

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

    $109.99 $15.99

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