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[P] A step-by-step Policy Gradient algorithms Colab + Pytorch tutorial

Hi, ML redditors! I and my colleagues made a Reinforcement Learning tutorial in Pytorch which consists of Policy Gradient algorithms from A2C to SAC. In addition, it includes learning acceleration methods using demonstrations for treating real applications with sparse rewards:

  1. A2C
  2. PPO
  3. DDPG
  4. TD3
  5. SAC
  6. DDPG from Demonstration
  7. Behavior Cloning (with DDPG)

Every chapter contains both theoretical backgrounds and object-oriented implementation, and thanks to Colab, you can execute them and render the results without any installation even on your smartphone!

I hope it will be helpful for someone. 🙂

Cheers.

submitted by /u/syee-kim
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.