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22 lines
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22 lines
1.1 KiB
Plaintext
# The “Deep” in Reinforcement Learning [[deep-rl]]
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<Tip>
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What we've talked about so far is Reinforcement Learning. But where does the "Deep" come into play?
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</Tip>
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Deep Reinforcement Learning introduces **deep neural networks to solve Reinforcement Learning problems** — hence the name “deep”.
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For instance, in the next unit, we’ll learn about two value-based algorithms: Q-Learning (classic Reinforcement Learning) and then Deep Q-Learning.
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You’ll see the difference is that, in the first approach, **we use a traditional algorithm** to create a Q table that helps us find what action to take for each state.
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In the second approach, **we will use a Neural Network** (to approximate the Q value).
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<figure>
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<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit1/deep.jpg" alt="Value based RL"/>
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<figcaption>Schema inspired by the Q learning notebook by Udacity
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</figcaption>
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</figure>
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If you are not familiar with Deep Learning you should definitely watch [the FastAI Practical Deep Learning for Coders](https://course.fast.ai) (Free).
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