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22 lines
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22 lines
1.2 KiB
Plaintext
# Conclusion [[conclusion]]
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Congrats on finishing this chapter! There was a lot of information. And congrats on finishing the tutorials. You’ve just implemented your first RL agent from scratch and shared it on the Hub 🥳.
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Implementing from scratch when you study a new architecture **is important to understand how it works.**
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That’s **normal if you still feel confused** with all these elements. **This was the same for me and for all people who studied RL.**
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Take time to really grasp the material before continuing.
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In the next chapter, we’re going to dive deeper by studying our first Deep Reinforcement Learning algorithm based on Q-Learning: Deep Q-Learning. And you'll train a **DQN agent with <a href="https://github.com/DLR-RM/rl-baselines3-zoo">RL-Baselines3 Zoo</a> to play Atari Games**.
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<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit4/atari-envs.gif" alt="Atari environments"/>
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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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