I8-Unit_5-Introduction_to_Unity_ML_Agents-H7-Conclusion
中英文对照学习，效果更佳！
原课程链接：https://huggingface.co/deep-rl-course/unitbonus3/language-models?fw=pt
Conclusion
结论
Congrats on finishing this unit! You’ve just trained your first ML-Agents and shared it to the Hub 🥳.
祝贺你完成了这个单元！您已经培训了您的第一个ML-™代理，并将其分享给了Ÿ袁³中心。
The best way to learn is to practice and try stuff. Why not try another environment? ML-Agents has 18 different environments.
学习的最好方法就是练习和尝试。为什么不尝试另一个环境呢？ML-Agents有18种不同的环境。
For instance:
例如：
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
WORM，在那里您可以教蠕虫爬行。Walker：教代理朝着目标行走。检查文档以找到如何训练他们以及集线器上已集成的MLAgent环境的列表：https://github.com/huggingface/ml-agents#getting-started
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.
示例环境在下一单元中，我们将学习多™。你将训练你的第一个多代理人在足球和雪球战斗中与其他同学™™的经纪人竞争。
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
Snownball Fighting最后，我们很想听听您对这门课程的看法以及我们如何改进它。如果您对此有任何反馈，请填写此表格，并与Ÿ‘‰联系