Hello welcome back to the course on artificial intelligence.
Today we're going to discuss the plan of attack for the section we're talking about kill learning.
And we've got quite a few tutorials so I think it is a good idea for us to quickly go through them to
understand what to expect in the upcoming videos.
So here we go.
All right.
What we will learn in this section.
First things first we will talk about what reinforcement learning actually is and what the philosophic
behind reinforcement learning is and how reinforcement learning actually can be seen in real life and
how it relates to things that we observe in real life are actually things that we do ourselves.
They don't talk about the bellmen equation very fundamental concept underpinning everything or a lot
of things that are happening and for reinforcement learning especially in the space of CULE learning
and what we're going to be discussing in this section of the course and in the following sections.
Then we'll talk about the plan and the plan that and raw iron artificial intelligence comes up with
in order to navigate inside environments we'll see what that how that comes together very quick but
quite interesting.
There we'll talk about market of decision processes and your concept we're going to introduce a very
new concept which will slowly even add a layer of sophistication to our Belman equation to our whole
reinforcement learning to our CULE learning concepts and that's the way this section is structured that
we introduce the Bollmann equation a very simplistic form and then slowly throughout the tutorials we
adds layers of sophistication to it in order to get to the final version.
That is our designated destination in terms of Hillary but we'll get there slowly.
In order for us to have enough time to process all that information and let it settle in.
And mark of dissident proses is an extra layer of sophistication on top of what we've discussed or what
we will have or it discussed by then there will talk about policies versus plans.
Another interesting Tauriel they're all interesting.
Just another quick tutorial on how policy is different from plans and what the differences there are
and these are terms that you will probably hear or read in the literature if you're going to be delving
into it to get additional information on reinforcement learning.
They're all talk about adding a living penalty to our environments.
And that's that's kind of another way of adding complexity into the environments that our agents are
going to be operating in.
They're all talk about the intuition behind keep learning so up until that tutorial we're going to be
talking values of states.
And then finally we're going to switch to talking about values or actions or cube values and then we're
going to introduce the temporal difference.
This is a tutorial where everything that we've learned is going to come together to explain how exactly
do agents or artificial does artificial intelligence learn how does it update its values through all
the iterative process that is going through.
And then finally we're going to look at a visible zation of learning so we're going to take everything
we learn and we're going to look at it happen in front of our eyes and watch an artificial intelligence
actually perform CULE learning and do all the things that we're going to discuss on an intuitive level
is going to actually do in practice and that will help us even further grasp that knowledge that we're
going to be coming off in the section.
So hopefully you're very excited about these upcoming tutorials.
I definitely am.
And there some very interesting slides coming up and more important the concepts themselves are very
very interesting and I'm sure you're going to enjoy them quite a lot.
And I look forward to seeing you next time.
Until then enjoy AI.