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Introduction

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.

A patchwork of all the environments you’ve trained your agents on since the beginning of the course

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.

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Course Maintenance Notice 🚧

Please note that this Deep Reinforcement Learning course is now in a low-maintenance state. However, it remains an excellent resource to learn both the theory and practical aspects of Deep Reinforcement Learning.

Keep in mind the following points:

- Unit 7 (AI vs AI) : This feature is currently non-functional. However, you can still train your agent to play soccer and observe its performance. But the leaderboard for AI vs AI soccer was shut down.

This environment was made by the Unity MLAgents Team

So let’s get started!

< > Update on GitHub