Andrei.

Andrei!

Andrei!

Directed by Andrei TARKOVSKY

Have you got any news from him?

Good night, Andrei.

And this is Dr. Andrei Smyslov.

Chief Agent Yuri Marklov.

You mean you're breaking up with Prince Andrei?

I'm Chief Agent Yuri Marklov of the F.S.B.

Not once did Prince Bolkonsky call on the Rostovs.

Prince Andrei wants his year reduced by three months.

In normal life, there is a cost associated with moving.

Thrun So let's begin by taking the hidden out of the Markov model and study Markov chains.

So most of this class focuses on Markov decision processes as opposed to partially observable Markov decision processes.

Now, before we get into the math of reinforcement learning,

The music doesn't work! TOMORROW IS THE END OF THE WORLD To the memory of my mother.

Obviously the top state is much more attractive than the bottom state with minus 100.

It's applicable to many estimation problems, and it will be important going forward in this class.

You learned about localization.

But remember, Prince Andrei, should you die on the battlefield, your old father will mourn for you.

His obligations as trustee of the family's Ryazan estate compelled Prince Andrei to call upon the local Marshal of Nobility, Count Rostov.

He acts as if he thought that my Bolkonsky wouldn't understand, wouldn't approve of this wonderful time we're having.

So, we've learned quite a bit so far.

like A , depth first, right first and so on.

This is a great moment where I can test whether you really think like an artificial intelligence person.

Thrun

Narrator Before diving into too much detail,

In this final question, I'd like you to learn the position parameters of Markov chains.

Again, what do you think could help us get the right answer this time?

So the answer here is the stationary distribution will have A occurring with 2 3 chance and B with 1 3 chance.

In this case, the problem really comes down to the naive Bayes assumption is a weak one, and the Markov assumption would do much better.

I'll briefly talk about POMDPs but not in any depth.

It's kind of the big thing in application and machine learning.

So what is a Markov decision process?

So we want the policy that maximizes that result.

Check that box if you think that would be helpful.

And that resolves simply to 0.4 if you work it all out.

This software carries out Markov Chain Monte Carlo calculations by the use of Gibbs Sampling.

That's like the fancy math notation, but what it really means is we just wait a long, long time.

That's called a value.

leads to state S3.

What if you don't even know P the transition model of the world?

In summary, then, we've learned how to do a lot with MDPs especially using reinforcement learning.

We never brought planning and uncertainty together, uncertainty and learning, or planning and learning.