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27 lines
1.6 KiB
Plaintext
27 lines
1.6 KiB
Plaintext
# Introduction [[introduction]]
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<img src=”https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit0/thumbnail.jpg” alt=”Thumbnail”/>
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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.
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Our different agents worked great, and the single-agent system is useful for many applications.
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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.
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Consequently, we must study how to train deep reinforcement learning agents in a multi-agent system to build robust agents that can adapt, collaborate, or compete.
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So today, we’re going to learn the basics of this fascinating topic of multi-agents reinforcement learning (MARL).
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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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And you’re going to participate in AI vs. AI challenges where your trained agent will compete against other classmates’ agents every day and be ranked on a new leaderboard.
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<figure>
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<img src=”https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit10/soccertwos.gif” alt=”SoccerTwos”/>
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<figcaption>This environment was made by the <a href=”https://github.com/Unity-Technologies/ml-agents”>Unity MLAgents Team</a></figcaption>
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</figure>
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So let’s get started!
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