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63 lines
3.3 KiB
Markdown
63 lines
3.3 KiB
Markdown
# Unit 7: Advantage Actor Critic (A2C) using Robotics Simulations with PyBullet 🤖
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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:
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1. PyBullet
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2. MuJoco
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3. Unity Simulations
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We're going to learn about Advantage Actor Critic (A2C) and how to use PyBullet. And train a spider agent to walk.
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🏆 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
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Let's get started 🥳
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## Required time ⏱️
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The required time for this unit is, approximately:
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- 1 hour for the theory.
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- 1 hour for the hands-on.
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## Start this Unit 🚀
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Here are the steps for this Unit:
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1️⃣ 📖 [Read Advantage Actor Critic Chapter](https://huggingface.co/blog/deep-rl-a2c).
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2️⃣ 👩💻 Then dive on the hands-on where you'll train two robots to walk.
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The hands-on 👉 [](https://colab.research.google.com/github/huggingface/deep-rl-class/blob/main/unit7/unit7.ipynb)
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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 🏆?
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The leaderboard 👉 https://huggingface.co/spaces/chrisjay/Deep-Reinforcement-Learning-Leaderboard
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## Additional readings 📚
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- [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)
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- [Bias-variance Tradeoff in Reinforcement Learning](https://www.endtoend.ai/blog/bias-variance-tradeoff-in-reinforcement-learning/)
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- [Foundations of Deep RL Series, L3 Policy Gradients and Advantage Estimation by Pieter Abbeel](https://youtu.be/AKbX1Zvo7r8)
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## How to make the most of this course
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To make the most of the course, my advice is to:
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- **Participate in Discord** and join a study group.
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- **Read multiple times** the theory part and takes some notes
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- 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 🥳
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- 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.
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## This is a course built with you 👷🏿♀️
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We want to improve and update the course iteratively with your feedback. **If you have some, please fill this form** 👉 https://forms.gle/3HgA7bEHwAmmLfwh9
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## Don’t forget to join the Community 📢
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We have a discord server where you **can exchange with the community and with us, create study groups to grow each other and more**
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👉🏻 [https://discord.gg/aYka4Yhff9](https://discord.gg/aYka4Yhff9).
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Don’t forget to **introduce yourself when you sign up 🤗**
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❓If you have other questions, [please check our FAQ](https://github.com/huggingface/deep-rl-class#faq)
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### Keep learning, stay awesome 🤗
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