# Introduction [[introduction]]

<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit0/thumbnail.png" alt="Thumbnail"/>

Since the beginning of this course, we learned to train agents in a *single-agent system*  where our agent was alone in its environment: it was **not cooperating or collaborating with other agents**.

This worked great, and the single-agent system is useful for many applications.


<figure>

<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit10/patchwork.jpg" alt="Patchwork"/>

<figcaption>
A patchwork of all the environments you’ve trained your agents on since the beginning of the course
</figcaption>
</figure>

But, as humans, **we live in a multi-agent world**. Our intelligence comes from interaction with other agents. And so, our **goal is to create agents that can interact with other humans and other agents**.

Consequently, we must study how to train deep reinforcement learning agents in a *multi-agents system* to build robust agents that can adapt, collaborate, or compete.

So today we’re going to **learn the basics of the fascinating topic of multi-agents reinforcement learning (MARL)**.

And the most exciting part is that, during this unit, you’re going to train your first agents in a multi-agents system: **a 2vs2 soccer team that needs to beat the opponent team**.

And you’re going to participate in **AI vs. AI challenge** where your trained agent will compete against other classmates’ agents every day and be ranked on a [new leaderboard](https://huggingface.co/spaces/huggingface-projects/AIvsAI-SoccerTwos).

<figure>
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit10/soccertwos.gif" alt="SoccerTwos"/>

<figcaption>This environment was made by the <a href="https://github.com/Unity-Technologies/ml-agents">Unity MLAgents Team</a></figcaption>

</figure>

So let’s get started!
