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Unit 8: Proximal Policy Optimization (PPO) using Robotics Simulations with PyBullet 🤖
One of the major industries that use Reinforcement Learning is robotics. Unfortunately, having access to robot equipment is very expensive. Fortunately, some simulations exist to train Robots:
- PyBullet
- MuJoco
- Unity Simulations
We're going to learn about Advantage Actor Critic (A2C) and how to use PyBullet. And train a spider agent to walk.
🏆 You'll then be able to compare your agent’s results with other classmates thanks to a leaderboard 🔥 👉 https://huggingface.co/spaces/chrisjay/Deep-Reinforcement-Learning-Leaderboard
Let's get started 🥳
Required time ⏱️
The required time for this unit is, approximately:
- 1 hour for the theory.
- 2 hours for the hands-on.
Start this Unit 🚀
Here are the steps for this Unit:
1️⃣ 📖 Read Proximal Policy Optimization Chapter.
2️⃣ 👩💻 Then dive on the hands-on where you'll train two robots to walk.
The hands-on 👉 
Thanks to a leaderboard, you'll be able to compare your results with other classmates and exchange the best practices to improve your agent's scores Who will win the challenge for Unit 7 🏆?
The leaderboard 👉 https://huggingface.co/spaces/chrisjay/Deep-Reinforcement-Learning-Leaderboard
Additional readings 📚
- Towards Delivering a Coherent Self-Contained Explanation of Proximal Policy Optimization by Daniel Bick
- What is the way to understand Proximal Policy Optimization Algorithm in RL?
- Foundations of Deep RL Series, L4 TRPO and PPO by Pieter Abbeel
- OpenAI PPO Blogpost
- Spinning Up RL PPO
- Paper Proximal Policy Optimization Algorithms
- The 37 Implementation Details of Proximal Policy Optimization
How to make the most of this course
To make the most of the course, my advice is to:
- Participate in Discord and join a study group.
- Read multiple times the theory part and takes some notes
- Don’t just do the colab. When you learn something, try to change the environment, change the parameters and read the libraries' documentation. Have fun 🥳
- Struggling is a good thing in learning. It means that you start to build new skills. Deep RL is a complex topic and it takes time to understand. Try different approaches, use our additional readings, and exchange with classmates on discord.
This is a course built with you 👷🏿♀️
We want to improve and update the course iteratively with your feedback. If you have some, please fill this form 👉 https://forms.gle/3HgA7bEHwAmmLfwh9
Don’t forget to join the Community 📢
We have a discord server where you can exchange with the community and with us, create study groups to grow each other and more
👉🏻 https://discord.gg/aYka4Yhff9.
Don’t forget to introduce yourself when you sign up 🤗
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