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30 lines
1.9 KiB
Plaintext
30 lines
1.9 KiB
Plaintext
# Hands-on
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<CourseFloatingBanner classNames="absolute z-10 right-0 top-0"
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notebooks={[
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{label: "Google Colab", value: "https://colab.research.google.com/github/huggingface/deep-rl-class/blob/main/notebooks/unit5/unit5.ipynb"}
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]}
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askForHelpUrl="http://hf.co/join/discord" />
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Now that we learned what is ML-Agents, how it works and that we studied the two environments we're going to use. We're ready to train our agents.
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- The first one will learn to **shoot snowballs onto spawning target**.
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- The second need to **press a button to spawn a pyramid, then navigate to the pyramid, knock it over, and move to the gold brick at the top**. To do that, it will need to explore its environment, and we will use a technique called curiosity.
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<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit7/envs.png" alt="Environments" />
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After that, you'll be able to watch your agents playing directly on your browser.
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The ML-Agents integration on the Hub **is still experimental**, some features will be added in the future. But for now, to validate this hands-on for the certification process, you just need to push your trained models to the Hub.
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There's no results to attain to validate this one. But if you want to get nice results you can try to attain:
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- For [Pyramids](https://huggingface.co/spaces/unity/ML-Agents-Pyramids): Mean Reward = 1.75
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- For [SnowballTarget](https://huggingface.co/spaces/ThomasSimonini/ML-Agents-SnowballTarget): Mean Reward ⁼ 15 or 30 targets shoot in an episode.
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For more information about the certification process, check this section 👉 https://huggingface.co/deep-rl-course/en/unit0/introduction#certification-process
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**To start the hands-on click on Open In Colab button** 👇 :
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[](https://colab.research.google.com/github/huggingface/deep-rl-class/blob/master/notebooks/unit5/unit5.ipynb)
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