# 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:
1. PyBullet
2. MuJoco
3. 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

![cover](https://github.com/huggingface/deep-rl-class/blob/main/unit7/assets/img/pybullet-envs.gif?raw=true)

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](https://huggingface.co/blog/deep-rl-ppo).

2️⃣ 👩‍💻 Then dive on the hands-on where you'll train two robots to walk.

The hands-on 👉 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/deep-rl-class/blob/main/unit7/unit7.ipynb)

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](https://fse.studenttheses.ub.rug.nl/25709/1/mAI_2021_BickD.pdf) 
- [What is the way to understand Proximal Policy Optimization Algorithm in RL?](https://stackoverflow.com/questions/46422845/what-is-the-way-to-understand-proximal-policy-optimization-algorithm-in-rl)
- [Foundations of Deep RL Series, L4 TRPO and PPO by Pieter Abbeel](https://youtu.be/KjWF8VIMGiY)
- [OpenAI PPO Blogpost](https://openai.com/blog/openai-baselines-ppo/)
- [Spinning Up RL PPO](https://spinningup.openai.com/en/latest/algorithms/ppo.html)
- [Paper Proximal Policy Optimization Algorithms](https://arxiv.org/abs/1707.06347)
- [The 37 Implementation Details of Proximal Policy Optimization](https://ppo-details.cleanrl.dev//2021/11/05/ppo-implementation-details/)

## 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](https://discord.gg/aYka4Yhff9).

Don’t forget to **introduce yourself when you sign up 🤗**

❓If you have other questions, [please check our FAQ](https://github.com/huggingface/deep-rl-class#faq)

### Keep learning, stay awesome 🤗