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The “Deep” in Reinforcement Learning

> What we’ve talked about so far is Reinforcement Learning. But where does the “Deep” come into play?

Deep Reinforcement Learning introduces deep neural networks to solve Reinforcement Learning problems — hence the name “deep”.

For instance, in the next unit, we’ll learn about two value-based algorithms: Q-Learning (classic Reinforcement Learning) and then Deep Q-Learning.

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.

In the second approach, we will use a Neural Network (to approximate the Q value).

Schema inspired by the Q learning notebook by Udacity

If you are not familiar with Deep Learning you should definitely watch the FastAI Practical Deep Learning for Coders (Free).

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