Deep Reinforcement Learning in Python Tutorial – A Course on How to Implement Deep Learning Papers




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In this intermediate deep learning tutorial, you will learn how to go from reading a paper on deep deterministic policy gradients to implementing the concepts in Tensorflow. This process can be applied to any deep learning paper, not just deep reinforcement learning.

In the second part, you will learn how to code a deep deterministic policy gradient (DDPG) agent using Python and PyTorch, to beat the continuous lunar lander environment (a classic machine learning problem).

DDPG combines the best of Deep Q Learning and Actor Critic Methods into an algorithm that can solve environments with continuous action spaces. We will have an actor network that learns the (deterministic) policy, coupled with a critic network to learn the action-value functions. We will make use of a replay buffer to maximize sample efficiency, as well as target networks to assist in algorithm convergence and stability.

🎥 Course created by Phil Tabor. Check out his YouTube channel: https://www.youtube.com/channel/UC58v9cLitc8VaCjrcKyAbrw

⭐️ Course Contents ⭐️
⌨️ (0:00:00) Introduction
⌨️ (0:04:58) How to Implement Deep Learning Papers
⌨️ (1:59:00) Deep Deterministic Policy Gradients are Easy in Pytorch

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Comment List

  • freeCodeCamp.org
    November 20, 2020

    This dude sucks

  • freeCodeCamp.org
    November 20, 2020

    41:00 I dont think you need to wait a million steps. If you minibatch size is 64 then you just need to wait 64 steps. Right ?

  • freeCodeCamp.org
    November 20, 2020

    Hello, I need help with a paper " A new Deep-Q-Learning-Based Transmission Scheduling Mechanism for the Cognitive Internet of things". How can I contact you, inbox?

  • freeCodeCamp.org
    November 20, 2020

    Why not use an IDE to see typos before running it?

  • freeCodeCamp.org
    November 20, 2020

    Where did you get your brain?

  • freeCodeCamp.org
    November 20, 2020

    At 43:01, you say: " i is each element of that minibatch transitions" which is wrong. i is just the index of the reply memory, i.e. state i+1 follows after state i.
    And thanks for your great explanation. helped me a lot.

  • freeCodeCamp.org
    November 20, 2020

    Where is the code of this video?

  • freeCodeCamp.org
    November 20, 2020

    This is actually a video tutorial with so much academic quality.
    I am really amazed by this video and ability to implement a paper in this pace.
    Would plz keep up your good job?
    Thanks bro.

  • freeCodeCamp.org
    November 20, 2020

    2:45:50 Michael Jackson still alive guys

  • freeCodeCamp.org
    November 20, 2020

    Hi, i have run the code, but it did not converge at all. So I waana to know your hyperparameter's setting. Thanks a lot =-=

  • freeCodeCamp.org
    November 20, 2020

    Python is bloat.

  • freeCodeCamp.org
    November 20, 2020

    Please make more videos on implementing research papers on your channel😃

  • freeCodeCamp.org
    November 20, 2020

    I really like to learn python, and I have a question what is this video about? cause I didn't get anything

  • freeCodeCamp.org
    November 20, 2020

    Excellent camera quality – the only problem is that the programmed color changes of the PC lights cause the color adjustment of the camera to change.

  • freeCodeCamp.org
    November 20, 2020

    thanks please more like those read scientific papers .

  • freeCodeCamp.org
    November 20, 2020

    Phil youre a fucking legend

  • freeCodeCamp.org
    November 20, 2020

    Awesome! Looking to learn more and post on my channel.

  • freeCodeCamp.org
    November 20, 2020

    Its very advanced for me i guess (still watched watched for 20mins) … Hope to get some advice from phil for beginners… To really reach to a level of implementing papers….any advice on learning road path would be helpful. Have subscribed to your channel also.Thanks Phil. 🙂

  • freeCodeCamp.org
    November 20, 2020

    Thank you! This is an incredible Reinforcement Learning tutorial!

  • freeCodeCamp.org
    November 20, 2020

    Reminded of Sherlock's assistant.

  • freeCodeCamp.org
    November 20, 2020

    Thank u for this video 😀

    Are u a brother of Bucky Roberts (thenewboston) ?

  • freeCodeCamp.org
    November 20, 2020

    Bunch of thanks 🙏

  • freeCodeCamp.org
    November 20, 2020

    This looks awesome
    But I have a question, do you think this could be applied to website fingerprinting, where an ai is able to figure out the port and server information as you visit a website to make sure it's secure?

  • freeCodeCamp.org
    November 20, 2020

    Thanks soo much you Bro 😉 New Magicians in Future They are Coders,Programmers,Engineers and All Designers.

  • freeCodeCamp.org
    November 20, 2020

    interesting..!!

  • freeCodeCamp.org
    November 20, 2020

    Awesome! This is what I was waiting for. Super thank you!
    ( ö )/

  • freeCodeCamp.org
    November 20, 2020

    Thank you!!

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