Welcome to the most fascinating topic in Artificial Intelligence:Deep Reinforcement Learning.
Welcome to the most fascinating topic in Artificial Intelligence: **Deep Reinforcement Learning**.
This course will **teach you about Deep Reinforcement Learning from beginner to expert**. It’s completely free and open-source!
@@ -23,28 +23,35 @@ In this course, you will:
- 📖 Study Deep Reinforcement Learning in **theory and practice.**
- 🧑💻 Learn to **use famous Deep RL libraries** such as [Stable Baselines3](https://stable-baselines3.readthedocs.io/en/master/), [RL Baselines3 Zoo](https://github.com/DLR-RM/rl-baselines3-zoo), [Sample Factory](https://samplefactory.dev/) and [CleanRL](https://github.com/vwxyzjn/cleanrl).
- 🤖 **Train agents in unique environments** such as [SnowballFight](https://huggingface.co/spaces/ThomasSimonini/SnowballFight), [Huggy the Doggo 🐶](https://huggingface.co/spaces/ThomasSimonini/Huggy), [VizDoom (Doom)](https://vizdoom.cs.put.edu.pl/)and classical ones such as [Space Invaders](https://www.gymlibrary.dev/environments/atari/), [PyBullet](https://pybullet.org/wordpress/) and more.
- 🤖 **Train agents in unique environments** such as [SnowballFight](https://huggingface.co/spaces/ThomasSimonini/SnowballFight), [Huggy the Doggo 🐶](https://huggingface.co/spaces/ThomasSimonini/Huggy), [VizDoom (Doom)](https://vizdoom.cs.put.edu.pl/)and classical ones such as [Space Invaders](https://gymnasium.farama.org/environments/atari/space_invaders/), [PyBullet](https://pybullet.org/wordpress/) and more.
- 💾 Share your **trained agents with one line of code to the Hub** and also download powerful agents from the community.
- 🏆 Participate in challenges where you will **evaluate your agents against other teams. You'll also get to play against the agents you'll train.**
- 🎓 **Earn a certificate of completion** by completing 80% of the assignments.
And more!
At the end of this course, **you’ll get a solid foundation from the basics to the SOTA (state-of-the-art) of methods**.
You can find the syllabus on our website 👉 <a href="https://simoninithomas.github.io/deep-rl-course/">here</a>
Don’t forget to **<a href="http://eepurl.com/ic5ZUD">sign up to the course</a>** (we are collecting your email to be able to**send you the links when each Unit is published and give you information about the challenges and updates).**
Sign up 👉 <a href="http://eepurl.com/ic5ZUD">here</a>
## What does the course look like? [[course-look-like]]
The course is composed of:
- *A theory part*: where you learn a **concept in theory (article)**.
- *A theory part*: where you learn a **concept in theory**.
- *A hands-on*: where you’ll learn **to use famous Deep RL libraries** to train your agents in unique environments. These hands-on will be **Google Colab notebooks with companion tutorial videos** if you prefer learning with video format!
- *Challenges*: you'll get to use your agent to compete against other agents in different challenges. There will also be leaderboards for you to compare the agents' performance.
- *Challenges*: you'll get to put your agent to compete against other agents in different challenges. There will also be [a leaderboard](https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard) for you to compare the agents' performance.
## What's the syllabus? [[syllabus]]
This is the course's syllabus:
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit0/syllabus1.jpg" alt="Syllabus Part 1" width="100%"/>
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit0/syllabus2.jpg" alt="Syllabus Part 2" width="100%"/>
## Two paths: choose your own adventure [[two-paths]]
@@ -52,8 +59,8 @@ The course is composed of:
You can choose to follow this course either:
- *To get a certificate of completion*: you need to complete 80% of the assignments before the beginning of June 2023.
- *To get a certificate of honors*: you need to complete 100% of the assignments before the beginning of June 2023.
- *To get a certificate of completion*: you need to complete 80% of the assignments before the end of July 2023.
- *To get a certificate of honors*: you need to complete 100% of the assignments before the end of July 2023.
- *As a simple audit*: you can participate in all challenges and do assignments if you want, but you have no deadlines.
Both paths **are completely free**.
@@ -65,8 +72,8 @@ You don't need to tell us which path you choose. **If you get more than 80% of t
The certification process is **completely free**:
- *To get a certificate of completion*: you need to complete 80% of the assignments before the beginning of June 2023.
- *To get a certificate of honors*: you need to complete 100% of the assignments before the beginning of June 2023.
- *To get a certificate of completion*: you need to complete 80% of the assignments before the end of July 2023.
- *To get a certificate of honors*: you need to complete 100% of the assignments before the end of July 2023.
@@ -74,7 +81,7 @@ The certification process is **completely free**:
To get most of the course, we have some advice:
1. <a href="https://discord.gg/ydHrjt3WP5">Join or create study groups in Discord </a>: studying in groups is always easier. To do that, you need to join our discord server. If you're new to Discord, no worries! We have some tools that will help you learn about it.
1. <a href="https://discord.gg/ydHrjt3WP5">Join study groups in Discord </a>: studying in groups is always easier. To do that, you need to join our discord server. If you're new to Discord, no worries! We have some tools that will help you learn about it.
2. **Do the quizzes and assignments**: the best way to learn is to do and test yourself.
3. **Define a schedule to stay in sync**: you can use our recommended pace schedule below or create yours.
## What is the recommended pace? [[recommended-pace]]
@@ -124,14 +123,11 @@ About the team:
## When do the challenges start? [[challenges]]
In this new version of the course, you have two types of challenges:
- A leaderboard to compare your agent's performance to other classmates'.
- AI vs. AI challenges where you can train your agent and compete against other classmates' agents.
- [A leaderboard](https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard) to compare your agent's performance to other classmates'.
- [AI vs. AI challenges](https://huggingface.co/learn/deep-rl-course/unit7/introduction?fw=pt) where you can train your agent and compete against other classmates' agents.
These AI vs.AI challenges will be announced **in January**.
## I found a bug, or I want to improve the course [[contribute]]
Contributions are welcomed 🤗
@@ -141,4 +137,4 @@ Contributions are welcomed 🤗
## I still have questions [[questions]]
In that case, <a href="https://simoninithomas.github.io/deep-rl-course/#faq">check our FAQ</a>. And if the question is not in it, ask your question in our <a href="https://discord.gg/ydHrjt3WP5">discord server #rl-discussions.</a>
Please ask your question in our <a href="https://discord.gg/ydHrjt3WP5">discord server #rl-discussions.</a>
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Thomas Simonini