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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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