logic, logically, you have got to this position from this, without moving the wrist.

Let's think about those two situations.

Now let's expand it more, and there's now two options finally

Out of state.

For example, if the goal is to be in a clean location all we have to do is suck.

We had a state, like state A, and then we transitioned to another state, like state B, and all we could say about those states was are they identical to each other or not and maybe is one of them a goal state or not.

Because with the ability to notice either the presence or absence of something, we are able to switch between one of two belief states.

So what we want to be able to do is find some generalization, so that these 2 states look the same, and what I learn for this state that learning can transfer over into this state.

We also practiced this before.

If we go right and suck up the dirt, now we're down to a belief state consisting of 1 single state that is we know exactly where we are.

So, I've started coding the solution, and the first thing I do is create a dictionary to contain the reversed edges, and I switch the start and accepting states.

Assuming that the value function is initialized with 0 everywhere, what will be the value after a single value iteration for the state up here.

Instead of seeing these two lower case (b)'s, we'd see the upper case (B).

So very carefully we had to mechanically scrape off all the wax.

How are we going to update the utility of the prior state?

We see just a 1, and we've seen all of it.

We have the choice between three possible origins here.

What does it take to describe a game?

So, it's a fake real.

Next, we'll say that the robot has a dirt sensing camera, and that camera can either be on or off.

That tour already has a waiting list.

They're in a state of play.

OK?

How are the apples?

So what I'd like you to do is figure out after just 1 token of the input, what are some parse states we could be in?

So this last part, where I from here, Step 1 went back over to here and found this rule, and then brought it back over here 1, 2, 3 this 3 step process is reduction.

So we go from one of these 8 states to one of these 4 states and, every one of those 4, we're in a clean location.

So here, we came from zero we hadn't seen any tokens yet.

But the ice was going from ice to water.

Currently, we've only seen the 1 and the .

And one thing to notice is that perhaps there are states of human well being that we rarely access, that few people access.

This formulation will be good to the extent that similar states have the same value.

Another part is staying strong and fit.

And in this mode, we can go into quote and tag mode, from which we exit again by seeing a closely quote, and when we see the closing tag, we go back into the non quote, non tag mode.

It's all gone fuckin' wrong, here. I need to...

When we design a Kalman filter, you need effectively 2 things.

The robot vacuum cleaner can be in either of the 2 conditions, but as well as that each of the positions can either have dirt in it or not have dirt in it.

How bad was this?

We have to know from ourselves, there are certain states of mind that are conducive to this flourishing, to this well being, what the Greeks called eudaimonia, flourishing.

If I run this code with this table, what I get is a table that looks like this.

like the one over here and the on over here, have an optimal policy and action associated, because there is really no start state.

Do you know how bad Hagu is?

Narrator To understand the branching factor,

It's remained in this state for so many years.

In this finite state machine, to go the other direction, and finishing it off by changing the start state with the accepting state.