Proximal Policy Optimization (PPO) is Easy With PyTorch | Full PPO Tutorial

Proximal Policy Optimization is an advanced actor critic algorithm designed to improve performance by constraining updates to our actor network. It’s relatively straight forward to implement in code, and in this full tutorial you’re going to get a mini lecture covering the essential concepts behind the ppo algorithm, as well as a complete implementation in the pytorch framework. We’ll test our algorithm in a simple open ai gym environment: the cartpole.

Code for this video is here:
https://github.com/philtabor/Youtube-Code-Repository/tree/master/ReinforcementLearning/PolicyGradient/PPO/torch

Learn how to turn deep reinforcement learning papers into code:

Deep Q Learning:
https://www.udemy.com/course/deep-q-learning-from-paper-to-code/?couponCode=DQN-OCT-21

Actor Critic Methods:
https://www.udemy.com/course/actor-critic-methods-from-paper-to-code-with-pytorch/?couponCode=AC-OCT-21

Curiosity Driven Deep Reinforcement Learning
https://www.udemy.com/course/curiosity-driven-deep-reinforcement-learning/?couponCode=ICM-OCTOBER-21

Natural Language Processing from First Principles:
https://www.udemy.com/course/natural-language-processing-from-first-principles/?couponCode=NLP1-OCT-21Reinforcement Learning Fundamentals
https://www.manning.com/livevideo/reinforcement-learning-in-motion

Here are some books / courses I recommend (affiliate links):
Grokking Deep Learning in Motion: https://bit.ly/3fXHy8W
Grokking Deep Learning: https://bit.ly/3yJ14gT
Grokking Deep Reinforcement Learning: https://bit.ly/2VNAXql

Come hang out on Discord here:
https://discord.gg/Zr4VCdv

Website: https://www.neuralnet.ai
Github: https://github.com/philtabor
Twitter: https://twitter.com/MLWithPhil

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