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# Unit 7: Robotics Simulations with PyBullet 🤖
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# 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 use PyBullet today. And train two agents to walk:
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We're going to learn about Advantage Actor Critic (A2C) and how to use PyBullet. And train two agents to walk:
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- A bipedal walker 🦿
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- A spider 🕸️
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@@ -13,21 +13,31 @@ We're going to use PyBullet today. And train two agents to walk:
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We'll learn to use PyBullet environments and why we normalize input features.
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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](Foundations of Deep RL Series, L3 Policy Gradients and Advantage Estimation by Pieter Abbeel)
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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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