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23 lines
1.4 KiB
Plaintext
23 lines
1.4 KiB
Plaintext
# Conclusion
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Congrats on finishing this unit! You’ve just trained your first ML-Agents and shared it to the Hub 🥳.
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The best way to learn is to **practice and try stuff**. Why not try another environment? [ML-Agents has 18 different environments](https://github.com/Unity-Technologies/ml-agents/blob/develop/docs/Learning-Environment-Examples.md).
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For instance:
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- [Worm](https://singularite.itch.io/worm), where you teach a worm to crawl.
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- [Walker](https://singularite.itch.io/walker): teach an agent to walk towards a goal.
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Check the documentation to find how to train them and the list of already integrated MLAgents environments on the Hub: https://github.com/huggingface/ml-agents#getting-started
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<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit5/envs-unity.jpeg" alt="Example envs"/>
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In the next unit, we're going to learn about multi-agents. And you're going to train your first multi-agents to compete in Soccer and Snowball fight against other classmate's agents.
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<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit7/snowballfight.gif" alt="Snownball fight"/>
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Finally, we would love **to hear what you think of the course and how we can improve it**. If you have some feedback then, please 👉 [fill this form](https://forms.gle/BzKXWzLAGZESGNaE9)
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### Keep Learning, stay awesome 🤗
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