-: Hello and welcome back to the course
on artificial intelligence.
In today's section,
we're going to tackle deep convolutional Q-learning.
So we're taking deep Q-learning
to even a further step.
So we originally started with Q-learning,
the simple Q-learning.
Then we took that to deep Q-learning.
And now we're taking it to deep convolutional Q-learning.
So let's see what we're going to discuss
in terms of intuition.
The intuition's section gonna be quite quick.
This is not much that we need to add
as long as we're familiar
with convolutional neural networks,
and we'll touch on this towards the end
of today's tutorial.
So today in this section,
we're going to talk about deep convolutional Q-learning,
the intuition behind things
and why it's so powerful,
why exactly it's so important
to move away from deep Q-learning
and why deep Q-learning is just a basic building block
where it's just a step for us on the path
to deep convolutional Q-learning,
and what kind of avenues
deep convolutional Q-learning opens up to,
what kind of avenues the knowledge opens up to,
and where it can be applied.
We'll have some examples of that.
And then we'll talk about eligibility trace
or in-step Q-learning,
a very powerful addition to the whole concept
of deep Q-learning.
And we'll talk about the intuition behind that.
It's quite a complex topic
but nevertheless, we'll break down the intuition
in quite simple terms
and then I'll give you some additional references
where you can read up about eligibility traits
if you'd like to go into more detail.
But it is important for us to get the intuition down pat
because we're going to be using that
in the practical terms
because we're delving into much more complex topics now
that we need to add these extra elements
to our agents or to our Q-learning algorithms
so that they can actually handle these complex environments
and navigate them successfully.
And of course, in this section,
because we're talking about convolutional neural networks,
it is highly advisable that you check out annex number two,
convolutional neural networks.
Once again, if you've done the Deep Learning A to Z course,
then you're already familiar with this information.
So you can safely proceed with these tutorials
on the deep convolutional Q-learning.
if you haven't done the Deep Learning A to Z course,
then it's a great idea
to look at convolutional neural networks
and look at those intuition tutorials there
so you understand better
how images are processed by neural networks
in order to look for features,
and what's the whole convolutional layers
or about the pooling layers,
the flattening layers
and how all that works in order to come up
with some parameters about that describe the environment
or that describe that image.
And therefore, we're gonna be using those
as inputs into our neural network
instead of that vector, which we're talking about.
But more on that in the next tutorial.
So if you haven't seen those tutorials yet,
we highly advise you to check them out
to get up to speed with
or refresh your knowledge on convolutional neural networks.
All in all, we've got an exciting section
and as you can see quite
not that many intuition tutorials,
meaning that you'll be able
to jump into the practical side of things very quickly.
So on that note, I can't wait
to see you on the first tutorial,
and until then, enjoy AI.