H7-Unit_4-Policy_Gradient_with_PyTorch-H7-Conclusion
中英文对照学习，效果更佳！
原课程链接：https://huggingface.co/deep-rl-course/unit8/additional-readings?fw=pt
Conclusion
结论
Congrats on finishing this unit! There was a lot of information.
And congrats on finishing the tutorial. You’ve just coded your first Deep Reinforcement Learning agent from scratch using PyTorch and shared it on the Hub 🥳.
祝贺你完成了这一单元！有很多信息。并祝贺您完成了本教程。您已经使用™从头开始编写了您的第一个深度强化学习代理程序，并将其分享到了Ÿ的中心。
Don’t hesitate to iterate on this unit by improving the implementation for more complex environments (for instance, what about changing the network to a Convolutional Neural Network to handle
frames as observation)?
™毫不犹豫地通过改进更复杂环境的实现来迭代此单元(例如，将网络更改为卷积神经网络以将帧作为观测处理)？
In the next unit, we’re going to learn more about Unity MLAgents, by training agents in Unity environments. This way, you will be ready to participate in the AI vs AI challenges where you’ll train your agents
to compete against other agents in a snowball fight and a soccer game.
在下一单元中，我们将通过在统一环境中培训代理来学习更多关于™MLAgents的知识。这样，你就可以准备好参加AI VS AI挑战了，在那里你将训练你的代理在打雪仗和足球比赛中与其他代理竞争。
Sounds fun? See you next time!
听起来很有趣？下次见!
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
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