Are neural networks a type of reinforcement learning or are they different?
What is a neural network?

Neural networks are algorithms for function approximation. I like to call them a construction kit for functions. Their basic building block is a neuron, commonly visualized like this:



You can see the $n$ inputs $x_1, \dots, x_n$ ($x_0$ is typically constant 1), each multiplied with a weight $w_i \in \mathbb{R}$. This gets summed up and an activation function $\varphi$ (e.g. sigmoid $\varphi(x) = \frac{1}{1+e^{-x}}$) gets applied. So a neuron is a function

$$f(x_0, \dots, x_n) = \varphi(\sum_{i=0}^n x_i w_i)$$

The learning is just adjusting the weights $w_i$ automatically to something that makes sense in your context.

Now we can have arbitrary numbers of inputs $x_i$, but to have an arbitrary number of outputs we need more than one neuron. To get our model much more flexible, we stack them to a so called multilayer Perceptron (MLP):



So you can see, the output of one neuron (perceptron) can be the input of another! With backpropagation (a good implementation of gradient descent for this type of model) you can automatically adjust the weights.

What is reinforcement learning?

In machine learning you can distinguish 5 types of problems:


Regression: Predicting a continuous variable


You have a photo of a person and you want to tell how old the person is (e.g. howhot.io)

Classification: Predicting a variable with finite possible values


MNIST: You get a 28px x 28px image. You know it is either 0, or 1, or 2, ..., or 9. So a digit. You have to say which one.
ImageNet: You get a bigger image. You have to decide which one of a 1000 classes it is (dog, cat, car, house, ...). You can be sure it is one (and exactly one) of those.
http://write-math.com: Classify a handwritten symbol into about 380 classes
https://howhot.io/: Classify gender

Clustering: Grouping data


You have a lot of portrait images (or crops of images). You know there is exactly one persons face on it. You don't know how many people there are in total. Now you would like to group it so that each group is one person.
Species: You properties of animals and you want to group them. So you want a hierarchical clustering which puts closely related animals (dogs, cats; fly, moscito) closer together than not related ones (dogs, flys)

Collaborative Filtering: Filling gaps


Netflix: You have a lot of movies, a lot of ratings. However, not every person rated every movie. In fact, probably no person did. But you want to fill the gaps so that you can tell the users which movies they should watch because they will probably like it.
Amazon: pretty much the same as with Netflix, but for all kinds of products

Reinforcement learning (RL): Learning with environment


Self-driving cars
Playing games (e.g. Backgammon, Go, Atari (video))



What makes RL very different from the others is that you typically don't have a lot of data to start with, but you can generate a lot of data by playing. You have to deal with the problem that you have to make decisions, but it is not clear what is good (delayed reward). For example, it might take several moves until you know in Go if a move was smart.

What is different between neural networks and RL?

Neural networks are algorithms, RL is a problem type. You can approach RL with neural networks.

What is the relationship between neural networks and genetic algorithms?

Search for "the five tribes of machine learning", e.g. the image on http://www.welchlabs.com/blog/2016/2/16/whats-next-for-welch-labs