Teaching Robots to Walk with Proximal Policy Optimization (PPO) | Reinforcement Learning for Robots

Among the successes of modern bipedal robotics, deep reinforcement learning has been conspicuously absent. That is, until a group from Berkley applied Proximal Policy Optimization to teaching a bipedal robot named Cassie how to walk. They leveraged simulations in the MuJoCo simulator, coupled with judicious use of domain randomization, to get a robot to walk in the real world. In this video, we’ll analyze their paper and see how they did it.

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-21

Reinforcement 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

Source of this AI Video

AI video(s) you might be interested in …