CS_ALearn/deep-rl-class
1
0
Fork 0
mirror of https://github.com/huggingface/deep-rl-class.git synced 2026-04-13 16:29:42 +08:00
Code Issues Projects Releases Wiki Activity

Add Ed author

Browse Source
This commit is contained in:
Thomas Simonini
2023-02-21 07:15:41 +01:00
committed by GitHub
parent 49b3e0b741
commit 7220220848
1 changed files with 3 additions and 1 deletions
Download Patch File Download Diff File Expand all files Collapse all files

4
units/en/unit8/introduction-sf.mdx
View File

@@ -4,8 +4,10 @@
In this second part of Unit 8, we'll get deeper into PPO optimization by using [Sample-Factory](https://samplefactory.dev/), an **asynchronous implementation of the PPO algorithm**, to train our agent playing [vizdoom](https://vizdoom.cs.put.edu.pl/) (an open source version of Doom).
During the notebook, **you'll train your agent to play Health Gathering level**, where our agent needs to collect health packs to avoid dying. And after that, you'll be able to **train your agent to play more complex versions of the levels, such as Deathmatch**.
During the notebook, **you'll train your agent to play Health Gathering level**, where our agent must collect health packs to avoid dying. And after that, you can **train your agent to play more complex versions of the levels, such as Deathmatch**.
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit10/environments.png" alt="Environment"/>
Sounds exciting? Let's get started! 🚀
The hands-on is made by [Edward Beeching](https://twitter.com/edwardbeeching), a Machine Learning Research Scientist at Hugging Face. He worked on Godot Reinforcement Learning Agents, an open-source interface for developing environments and agents in the Godot Game Engine.