# Tag Info

4

It's the same thing, first version is the special case of the more general one. In the first case you only have two classes, it's binary cross-entropy, and they also included iteration over batch of samples. In the second case you have multiple classes and in the current form it's only for a single sample. In the first case there is only one output, if you ...

3

In some sense, you're right that a neural net is just another tool to fit data. However, it's quite the tool! There's this universal approximation theorem saying that, under decent conditions, a neural network can get as close as you want to a wide class of functions. This means that you can get the network to give you complicated shapes with squiggles all ...

1

To add to nbro's answer, I'd say also that much of the time the distance measure isn't simply a design decision, rather it comes up naturally from the model of the problem. For instance, minimizing the KL divergence between your policy and the softmax of the Q values at a given state is equivalent to policy optimization where the optimality at a given state ...

1

I did not read those two specified linked/cited papers and I am not currently familiar with the total variation distance, but I think I can answer some of your questions, given that I am reasonably familiar with the KL divergence. When you compute the $D_{KL}$ between two polices, what does that tell you about them The KL divergence is a measure of "...

0

As a supplement to nbro's nice answer, I think a major difference between RL and optimal control lies in the motivation behind the problem you're solving. As has been pointed out by comments and answers here (as well as the OP), the line between RL and optimal control can be quite blurry. Consider the LQG algorithm (linear quadratic gaussian) which is ...

2

Section 5.2 Error Decomposition of the book Understanding Machine Learning: From Theory to Algorithms (2014) gives a description of the approximation error and estimation error in the context of empirical risk minimization (ERM), so in the context of learning theory. I will just summarise their definition. If you want to know more about these topics, I ...

3

Parametric Methods A parametric approach (Regression, Linear Support Vector Machines) has a fixed number of parameters and it makes a lot of assumptions about the data. This is because they are used for known data distributions. i.e, it makes a lot of presumptions about the data Non-Parametric Methods A non-parametric approach (k-Nearest Neighbours, Decision ...

0

We have a parametric model and non-parametric models a learning model that summarize data with a set of parameters of fixed size(independent of the number of training exmample) is called a parametric modeland if it couldn't do that we say non parametric model. The non-parametric model is good when you have a lot of data and no prior knowledge and when you ...

Top 50 recent answers are included