Yes or no.

But even at a higher level, the modeling limitations of complicated systems are such that, one might as well view the world as inherently stochastic.

A second terminology for environments pertains to whether the environment is deterministic or stochastic.

Now, these types of models have different properties.

First, it can only handle true and false values.

K means is a nice, intuitive algorithm to derive clusterings.

Please say yes or no.

And what we know about its distribution, its, its properties, what percentage of scores fall in certain regions.

In a deterministic action, it obviously succeeds, unless of course we run into a wall.

But there wasn't any internal structure to those states.

You really don't want to wait for the truck to disappear.

This is a degenerate case.

I want to ask you, for each one, which of these properties they exhibit.

There's just the likelihood.

In the previous unit, we went over the basics of probability theory and saw how a Bayes network could concisely represent a joint probability distribution, including the representation of independence between the variables.

The observation partitions the belief state into smaller belief states.

A probability theorist...

And it makes much more sense to talk about the probability or random variable equaling a value, or the probability that it is less than or greater than something or the probability that is has some property

We've seen how a search can discover sequences of actions to solve problems.

You may not go forward exactly 1 meter.

Now, these distinctions are not hard to cut.

That we call deterministic.

You might remember in the very first class we distinguished very different characteristics of agent tasks, and here are some of those.

So that was the word model . What about probabilistic ? The word

In reality, you might find yourself wow, I'm over here. How did that happen?

Otherwise, just don't check it.

I wish to know whether it is partially observable, stochastic, continuous, or adversarial.

All these problems have in common that they have really challenging planning problems.

That's a probability distribution because the world is stochastic.

Most of the time, that's a perfectly good policy.

We have the same world as before, and actions have stochastic outcomes.

In benign environments, the environment might be random.

That gives us all the formalism we need to talk about these word sequence models probabilistic word sequence models.

So here are the 4 concepts again partially observable versus fully, stochastic versus deterministic, continuous versus discrete, adversarial versus benign.

And here's my code, this implements Bayes rule.

And so that's a mathematical formulation of what it means for this plan to be a Goal.

It might be effective.

What you have to do with the code we provide is to modify the dynamic programming routine that assumes a deterministic action with the one for a stochastic action.

And the answer is 0.45.

For example, that's how we can make the quad hover.

So these are all variations, and the type of model you choose depends on the application, but they all follow from this idea of a probabilistic model over sequences.

Narrator So to start with some lingo about unsupervised learning.

If, for example, there was a wall over here and the robot decided to go up, there'd be a 25 chance it would hit the wall, which is not good.

The MDP algorithms which I will talk about first fall into the intersection of fully observable yet stochastic, and just to remind us what the difference was, stochastic is an environment where the outcome of an action is somewhat random.

And then we told them the average likelihood of someone like them to suffer these misfortunes.