Update Deep Q-Learning unit

units/en/unit3/deep-q-algorithm.mdx

@@ -99,4 +99,4 @@ The solution is: when we compute the Q target, we use two networks to decouple t
 
 Therefore, Double DQN helps us reduce the overestimation of q values and, as a consequence, helps us train faster and have more stable learning.
 
-Since these three improvements in Deep Q-Learning, many have been added such as Prioritized Experience Replay, Dueling Deep Q-Learning. They’re out of the scope of this course but if you’re interested, check the links we put in the reading list.  TODO Add reading list
+Since these three improvements in Deep Q-Learning, many have been added such as Prioritized Experience Replay, Dueling Deep Q-Learning. They’re out of the scope of this course but if you’re interested, check the links we put in the reading list.

units/en/unit3/hands-on.mdx

@@ -1 +1,8 @@
 # Hands-on [[hands-on]]
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+Now that you've studied the theory behind Deep Q-Learning, **you’re ready to train your Deep Q-Learning agent to play Atari Games**. We'll start with Space Invaders, but you'll be able to use any Atari game you want 🔥
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+<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit4/atari-envs.gif" alt="Environments"/>
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+We're using the [RL-Baselines-3 Zoo integration](https://github.com/DLR-RM/rl-baselines3-zoo), a vanilla version of Deep Q-Learning with no extensions such as Double-DQN, Dueling-DQN, and Prioritized Experience Replay.