-: Hello and welcome back
to the course on
artificial intelligence.
In today's section,
we're tackling the topic
of Deep
Q-learning.
So let's see how we're going
to attack this.
In this section we will learn,
a Deep Q-learning intuition,
the learning side of things.
So, we are going to separate
Deep Q-learning,
the intuition behind it,
into two parts,
the learning and the acting.
And we're going to have
two tutorials on that.
So first we'll understand
how the neural networks actually learn
and how they update their weights
based on what we are feeding them in
and how the whole concept
of key learning works.
So how we're going to take
the temporal difference concepts
that we discuss in simple key learning.
We're going to apply them into
Deep Q-learning.
And then we're going to talk
about how Deep Q-learning algorithms
actually decide what action to take
in what state.
Then we're going to talk about experience replay
a very important addition on top
of Deep Q-learning,
which actually enables
Deep Q-learning to work properly
and you'll see why it's important
from that tutorial.
And then we're going to talk
about action selection policies.
We're going to talk about how
Deep Q-learning agents
are able
to combine exploration
with exploitation.
So, once they found something,
a good approach,
they can use that approach,
but also they need to explore
so that they don't get stuck
in a local maximum.
And one more thing,
I wanted to mention about the section
is it is highly beneficial
if you have a look at annex number one,
'Artificial Neural Networks'.
So if you go and explore all those topics
we've got some very powerful intuition tutorials
prepared for you there.
If you haven't done, of course,
if you haven't done the Deep learning course,
if you've done the deep learning course,
then you already know all of these things
and you can proceed with the section.
But if you want to get that additional knowledge
about neural networks before you proceed
with this part of the course,
this is highly advisable because
it will help you understand exactly
how neural networks work
and why they're so powerful,
why we're leveraging them
in this Deep Q-learning algorithm.
And once you've refreshed your knowledge
or gain that knowledge
on a neural networks from that annex
then come back here
and we will proceed with the Deep Q-learning.
If you're pretty comfortable with neural networks
then let's get straight into it.
Let's start talking about
Deep Q-learning intuition.
And I look forward to seeing you
on the first tutorial.
Until then, enjoy AI.