-: Hello and welcome back to the course
on Artificial Intelligence.
Today we're going to discuss the plan of attack
for this section.
We're talking about Q-learning
and we've got quite a few tutorials
so I think it's 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'll talk about
what reinforcement learning actually is
and what the philosophy 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,
or actually things that we do ourselves.
Then we'll talk about the Bellman equation.
A very fundamental concept underpinning everything
or a lot of things that are happening
in reinforcement learning, especially in the space
of Q-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 in artificial intelligence
comes up with in order to navigate inside an environment
and we'll see how that comes together.
A very quick but quite interesting tutorial.
Then we'll talk about market decision processes,
a new concept.
We're going to introduce a very new concept,
which will slowly even add an extra layer of sophistication
to our Bellman equation,
to our whole reinforcement learning,
to our Q-learning concepts.
And that's the way this section is structured
that we introduce the Bellman equation
in very simplistic form, and then slowly
throughout the tutorials we add layers of sophistication
to it in order to get to the final version
that is our designated destination in terms of Q-learning.
But we'll get there slowly in order for us to have
enough time to process all the information
and let it settle in and mark of decision processes
is an extra layer of sophistication
on top of what we've already discussed
or what we will have already discussed by then.
Then we'll talk about policies versus plans.
Another interesting tutorial.
They're all interesting.
Just another quick tutorial
on how policies differ from plans
and what the differences there are.
And these are terms that you will probably hear or read
in other literature if you're going to be delving into it
to get additional information on reinforcement learning.
Then we'll talk about adding a living penalty
to our environments, and that's kind of another way
of adding complexity into the environments
that our agents are going to be operating in.
Then we'll talk about the intuition behind Q-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 of actions or Q values.
And then we're going to introduce the temporal difference.
So this is the tutorial where everything that we've learned
is going to come together to explain how exactly
do agents or artificial,
how does artificial intelligence learn.
How does it update its values
throughout the iterative process that it's going through.
And then finally, we're going to look at
a visualization of Q-learning.
So we're going to take everything we learned
and we're going to look at it happen in front of our eyes
and watch an artificial intelligence
actually perform Q-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 covering off in this section.
So hopefully you're very excited
about these upcoming tutorials.
I definitely am and there's some very interesting
slides coming up and more importantly
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.