CS_ALearn/deep-rl-class
1
0
Fork 0
mirror of https://github.com/huggingface/deep-rl-class.git synced 2026-06-15 06:27:24 +08:00
Code Issues Projects Releases Wiki Activity

Update README.md

Browse Source
This commit is contained in:
Thomas Simonini
2022-08-04 21:34:24 +02:00
committed by GitHub
parent 60d2c0ac6b
commit 81558a6a0e
1 changed files with 6 additions and 9 deletions
Download Patch File Download Diff File Expand all files Collapse all files

15
unit8/README.md
View File

@@ -1,15 +1,12 @@
# Unit 8: Proximal Policy Optimization (PPO) using Robotics Simulations with PyBullet 🤖
# Unit 8: Proximal Policy Optimization (PPO) with PyTorch
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
Today we'll learn about Proximal Policy Optimization (PPO), an architecture that improves our agent's training stability by avoiding too large policy updates. To do that, we use a ratio that will indicates the difference between our current and old policy and clip this ratio from a specific range $[1 - \epsilon, 1 + \epsilon]$. Doing this will ensure that our policy update will not be too large and that the training is more stable.
We're going to learn about Advantage Actor Critic (A2C) and how to use PyBullet. And train a spider agent to walk.
And then, after the theory, we'll code a PPO architecture from scratch using PyTorch and bulletproof our implementation with CartPole-v1 and LunarLander-v2.
🏆 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)
<img src="assets/img/LunarLander.gif" alt="LunarLander"/>
Let's get started 🥳
@@ -23,9 +20,9 @@ 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.
2️⃣ 👩‍💻 Then dive on the hands-on:
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)
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/unit8/unit8.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 🏆?