A0-Unit_0-Welcome_to_the_course-A0-Welcome_to_the_course

中英文对照学习，效果更佳！
原课程链接：https://huggingface.co/deep-rl-course/unit0/introduction?fw=pt

Welcome to the 🤗 Deep Reinforcement Learning Course

欢迎参加🤗深度强化学习课程

Deep RL Course thumbnail
Welcome to the most fascinating topic in Artificial Intelligence: Deep Reinforcement Learning.

深度RL课程缩略图欢迎来到人工智能中最吸引人的话题：深度强化学习。

This course will teach you about Deep Reinforcement Learning from beginner to expert. It’s completely free and open-source!

本课程将教您从初学者到专家的深度强化学习。它是完全免费的，而且是开源的！

In this introduction unit you’ll:

在本入门单元中，您将：

  • Learn more about the course content.
  • Define the path you’re going to take (either self-audit or certification process)
  • Learn more about the AI vs. AI challenges you’re going to participate to.
  • Learn more about us.
  • Create your Hugging Face account (it’s free).
  • Sign-up our Discord server, the place where you can exchange with your classmates and us (the Hugging Face team).

Let’s get started!

详细了解课程内容。定义您将采取的路径(自我审计或认证流程)了解更多有关您将参与的AI与AI挑战的信息。了解有关我们的更多信息。创建您的Hugging Face账号(免费)。注册我们的不和谐服务器，在那里您可以与同学和我们(Hugging Face团队)进行交流。让我们开始吧！

What to expect?

还能期待什么？

In this course, you will:

在本课程中，您将：

And more!

📖在理论和实践中学习深度强化学习。🧑‍💻学习使用著名的深度RL库，如稳定基线3，RL基线3动物园，样本工厂和清洁RL。🤖培训代理在独特的环境中，如雪球，Huggy the Doggo🐶，Vizdoom(末日)，以及经典的，如太空入侵者，皮布利特等。💾分享您训练有素的代理与中心的一行代码，也从社区下载强大的代理。🏆参与挑战，在那里你将评估您的代理与其他团队。你还可以和你要训练的特工比赛。还有更多！

At the end of this course, you’ll get a solid foundation from the basics to the SOTA (state-of-the-art) methods.

在本课程结束时，您将获得从基础到SOTA(最先进的)方法的坚实基础。

You can find the syllabus on our website 👉 here

您可以在我们的网站👉上找到教学大纲

Don’t forget to sign up to the course (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).

别忘了注册课程(我们正在收集您的电子邮件，以便能够在每个单元发布时向您发送链接，并为您提供有关挑战和更新的信息)。

Sign up 👉 here

在此处注册👉

What does the course look like?

球场是什么样子的？

The course is composed of:

该课程由以下内容组成：

  • A theory part: where you learn a concept in theory (article).
  • 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 put your agent to compete against other agents in different challenges. There will also be leaderboards for you to compare the agents’ performance.

Two paths: choose your own adventure

理论部分：在那里您可以学习理论中的概念(文章)。实践：在那里，您将学习使用著名的Deep RL库在独特的环境中培训您的代理。如果你更喜欢通过视频格式学习，这些动手操作将是带有配套教程视频的Google Colab笔记本！挑战：你将可以让你的代理在不同的挑战中与其他代理竞争。还会有排行榜供你比较代理人的表现。两条途径：选择你自己的冒险

Two paths
You can choose to follow this course either:

您可以选择两条路径来学习本课程：

  • To get a certificate of completion: you need to complete 80% of the assignments before the end of April 2023.
  • To get a certificate of honors: you need to complete 100% of the assignments before the end of April 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.
Whatever path you choose, we advise you to follow the recommended pace to enjoy the course and challenges with your fellow classmates.

要获得结业证书：你需要在2023年4月底之前完成80%的作业。要获得荣誉证书：你需要在2023年4月底之前完成100%的作业。作为一个简单的审计：你可以参加所有挑战和做作业，如果你想的话，但你没有最后期限。这两种途径都是完全免费的。无论你选择哪条路，我们都建议你遵循推荐的节奏，与同学们一起享受课程和挑战。

You don’t need to tell us which path you choose. At the end of March, when we will verify the assignments if you get more than 80% of the assignments done, you’ll get a certificate.

你不需要告诉我们你选择了哪条路。在三月底，当我们验证作业时，如果你完成了80%以上的作业，你就会得到一张证书。

The Certification Process

认证过程

The certification process is completely free:

认证过程是完全免费的：

  • To get a certificate of completion: you need to complete 80% of the assignments before the end of April 2023.
  • To get a certificate of honors: you need to complete 100% of the assignments before the end of April 2023.

Course certification

毕业证书：你需要在2023年4月底之前完成80%的作业。要获得荣誉证书：你需要在2023年4月底之前完成100%的作业。

How to get most of the course?

如何获得课程的大部分内容？

To get most of the course, we have some advice:

为了了解这门课程的大部分内容，我们有一些建议：

  1. Join or create study groups in Discord : 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.

Course advice

加入或创建不和谐的学习小组：在小组中学习总是更容易的。要做到这一点，您需要加入我们的不一致服务器。如果你是不和谐的新手，不用担心！我们有一些工具可以帮助你了解它。做测验和作业：最好的学习方法是自己做和测试自己。定义一个保持同步的时间表：你可以使用下面推荐的速度时间表，也可以制定你的时间表。课程建议

What tools do I need?

我需要什么工具？

You need only 3 things:

你只需要3样东西：

  • A computer with an internet connection.
  • Google Colab (free version): most of our hands-on will use Google Colab, the free version is enough.
  • A Hugging Face Account: to push and load models. If you don’t have an account yet, you can create one here (it’s free).

Course tools needed

一台可以上网的电脑。谷歌可乐(免费版)：我们大多数实际操作都会使用谷歌可乐，免费版就足够了。Hugging Face账号：推送和加载模型。如果您还没有帐户，您可以在此处创建一个帐户(免费)。需要课程工具

What is the publishing schedule?

出版时间表是什么？

We publish a new unit every Tuesday.

我们每周二都会出版一份新的杂志。

Schedule 1
Schedule 2

附表1附表2

推荐的速度是多少？

We defined a planning that you can follow to keep up the pace of the course.

我们定义了一个您可以遵循的计划，以跟上课程的步伐。

Course advice
Course advice
Each chapter in this course is designed to be completed in 1 week, with approximately 3-4 hours of work per week. However, you can take as much time as necessary to complete the course. If you want to dive into a topic more in-depth, we’ll provide additional resources to help you achieve that.

课程建议课程建议本课程的每一章都被设计为在一周内完成，每周大约工作3-4个小时。但是，您可以根据需要花费尽可能多的时间来完成课程。如果您想深入探讨某个主题，我们将提供其他资源来帮助您实现这一目标。

Who are we

我们是谁？

About the author:

关于作者：

  • Thomas Simonini is a Developer Advocate at Hugging Face 🤗 specializing in Deep Reinforcement Learning. He founded the Deep Reinforcement Learning Course in 2018, which became one of the most used courses in Deep RL.

About the team:

Thomas Simonini是Huging Face🤗的开发倡导者，专门从事深度强化学习。2018年，他创办了深度强化学习课程，成为深度强化学习中使用最多的课程之一。关于团队：

  • Omar Sanseviero is a Machine Learning Engineer at Hugging Face where he works in the intersection of ML, Community and Open Source. Previously, Omar worked as a Software Engineer at Google in the teams of Assistant and TensorFlow Graphics. He is from Peru and likes llamas 🦙.
  • Sayak Paul is a Developer Advocate Engineer at Hugging Face. He’s interested in the area of representation learning (self-supervision, semi-supervision, model robustness). And he loves watching crime and action thrillers 🔪.

When do the challenges start?

奥马尔·桑塞维耶罗是Hugging Face的机器学习工程师，他在ML、社区和开放源码的交叉点工作。此前，奥马尔曾在谷歌的Assistant和TensorFlow Graphics团队担任软件工程师。他来自秘鲁，喜欢骆驼🦙。Sayak Paul是Hugging Face的开发倡导者工程师。他对表征学习领域(自我监督、半监督、模型稳健性)感兴趣。他喜欢看犯罪和动作惊悚片🔪。挑战什么时候开始？

In this new version of the course, you have two types of challenges:

在本课程的新版本中，您有两种类型的挑战：

  • 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.

Challenges
These AI vs.AI challenges will be announced in January.

一个排行榜，用来比较你的代理与其他同学的表现。AI与AI挑战，在那里你可以训练你的代理，并与其他同学的代理竞争。挑战这些AI与AI挑战将在一月份宣布。

I found a bug, or I want to improve the course

我发现了一个错误，或者我想改进课程

Contributions are welcomed 🤗

欢迎投稿🤗

I still have questions

如果您在笔记本中发现错误🐛，请打开问题并描述问题。如果您想改进课程，可以打开Pull Request。我仍有问题

In that case, check our FAQ. And if the question is not in it, ask your question in our discord server #rl-discussions.

在这种情况下，请查看我们的常见问题解答。如果问题不在其中，请在我们的不一致服务器#rl-讨论中提出您的问题。