# Conclusion [[conclusion]]

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 🥳.

Implementing from scratch when you study a new architecture **is important to understand how it works.**

It's **normal if you still feel confused** by all these elements. **This was the same for me and for everyone who studies RL.**

Take time to really grasp the material before continuing.


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**.


<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit4/atari-envs.gif" alt="Atari environments"/>


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)

### Keep Learning, stay awesome 🤗