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Update units/en/unit2/introduction.mdx

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Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
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Thomas Simonini
2022-12-03 11:11:41 +01:00
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@@ -15,7 +15,12 @@ We'll also **implement our first RL agent from scratch**: a Q-Learning agent an
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit3/envs.gif" alt="Environments"/>
We'll learn about the value-based methods and the difference between Monte Carlo and Temporal Difference Learning. And then, **we'll study and code our first RL algorithm**: Q-Learning, and implement our first RL Agent.
Concretely, we'll:
* learn about value-based methods
* learn about the differences between Monte Carlo and Temporal Difference Learning
* study and implement our first RL algorithm: Q-Learning
* implement our first RL agent
This unit is **fundamental if you want to be able to work on Deep Q-Learning**: the first Deep RL algorithm that was able to play Atari games and beat the human level on some of them (breakout, space invaders…).