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# Unit 7: Robotics Simulations with PyBullet 🤖
# Unit 7: Advantage Actor Critic (A2C) 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 use PyBullet today. And train two agents to walk:
We're going to learn about Advantage Actor Critic (A2C) and how to use PyBullet. And train two agents to walk:
- A bipedal walker 🦿
- A spider 🕸️
@@ -13,21 +13,31 @@ We're going to use PyBullet today. And train two agents to walk:
![cover](https://github.com/huggingface/deep-rl-class/blob/main/unit7/assets/img/pybullet-envs.gif?raw=true)
We'll learn to use PyBullet environments and why we normalize input features.
Let's get started 🥳
## Required time ⏱️
The required time for this unit is, approximately:
- 1 hour for the theory.
- 1 hour for the hands-on.
## Start this Unit 🚀
Here are the steps for this Unit:
1⃣ 📖 [Read Advantage Actor Critic Chapter](https://huggingface.co/blog/deep-rl-a2c).
2⃣ 👩‍💻 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 📚
- [Making Sense of the Bias / Variance Trade-off in (Deep) Reinforcement Learning](https://blog.mlreview.com/making-sense-of-the-bias-variance-trade-off-in-deep-reinforcement-learning-79cf1e83d565)
- [Bias-variance Tradeoff in Reinforcement Learning](https://www.endtoend.ai/blog/bias-variance-tradeoff-in-reinforcement-learning/)
- [Foundations of Deep RL Series, L3 Policy Gradients and Advantage Estimation by Pieter Abbeel](Foundations of Deep RL Series, L3 Policy Gradients and Advantage Estimation by Pieter Abbeel)
## How to make the most of this course
To make the most of the course, my advice is to: