(dance music)
(engines revving)
- What's up, guys?
Daniel Ricciardo here.
- Hey, guys, I am Tatiana Calderon.
- I've been picking up a few new hobbies
whilst home in Perth, Australia.
(laughing)
- This is my place in Madrid, Spain.
I've been looking for fun ways
to keep my racing instincts sharp.
- Today I'm gonna learn
about a new kind of racing.
- I can't wait to start competing
in the AWS DeepRacer League.
(dance music)
- Hey, Daniel, hey, Rob.
How you guys doin'?
- What's goin' on?
- Ready to go.
- Hi, guys, how are you?
- I started racing at the age of nine.
And I was always a little
bit of a risk-taker
and the speed is kinda
what really drew me in.
- Hey, Eddie, hey, Rob.
- Hey, Tatiana.
- I cannot tell you guys how
excited I am about this event.
- Ready for some DeepRacer fun?
- Big rookie!
I'm the first-ever Latin-American female
to drive a FORMULA 1 car.
I was dreaming of that moment
since I was nine-years-old.
- Hey, guys, Rob Smedley here.
We use machine learning tools all the time
in FORMULA 1 testing,
so I'm here to help coach
our drivers in training
their AWS DeepRacer models.
I like the fact that I'm
hoping both Tatiana and Daniel.
It's what's called hedging my bets.
- With each of these meetings
we're gonna cover a little bit
deeper into machine learning
and reinforcement learning theory.
We see all your models, Daniel,
moving up over the leaderboard
and we get you somewhere near the top
by the end of the month.
- Why somewhere near the top?
Why not the top, Brian?
(laughs)
We need to be at the top.
- So I'm ready, I'm ready to be educated.
- Have you ever gone
through this experience
of trying to train someone
to behave in a specific way?
- Yeah, I've got a nephew
and I'm certainly trying
to get him to have a little
bit of discipline in his life.
He's only three but he loves treats.
So I'm like, you've gotta earn them,
and I'm trying to teach him
some discipline around that.
- Treats is a good starting point.
We start by rewarding the type of behavior
that we wanna see.
And then we're not gonna
reward negative behavior.
And you do this long
enough and the outcome
is a well-trained dog.
That's the basic idea.
We wanna give our car
a reward for behaving
in a certain way.
And the way we're gonna do
that is to assign a value
to each cell.
- Okay.
It's been awhile since I was in school.
Don't tease me, guys, don't tease me.
- What we have to do is let the car know
that it's really good for you
to stay in this straight line
going from start to finish.
We've put a high reward, a reward of two,
right along that optimal path.
With enough iterations this
car learns that the best reward
it can get over the course of this episode
is to trace this straight line.
- Drivers, their reward is the lap time.
- We know if we do a corner one way
and we see the lap time
improve out of that corner,
that's, yeah, we've learned
that that way was better
as opposed to the previous lap
by taking the corner differently.
- Now we kinda have a sense
of what the reward function does.
So let's put all this stuff together
in the DeepRacer AWS console
and train your first model.
And this is the famed reward function
that we've been talking about.
This particular reward function
is kind of what we consider
a safe reward function.
It's gonna incentivize the car
to stay at the center of the track.
So go ahead and pick "Create Model" and let's go for gold.
- Hey, Tatiana, you know the track
that we're gonna be modeling
this on is Barcelona.
- That's one of the tracks
where I can officially say
that I can do it with my eyes closed.
- I have the lap record there.
I think it was set in 2018.
- [Brian] So if you
scroll down a little bit
you can click on the leaderboard
section of time trial
and we can check out the competition.
- I hate them all.
(laughing)
- [Brian] First place right now is RayG
with a time of a minute and 36 seconds.
- Ray is a bit of a G!
- He is obviously keeping
quite close to the middle
of the track but just catching.
- It can be beat, it can
be optimized further.
I've just seen our time.
We're slow, boys, we're slow.
- Oh!
- Ooh!
(laughing)
- We got 04:43
It's not great.
(laughing)
- [Eddie] So there you are with F1.
- [Tatiana] Oh no.
- I'm gonna guess you're not happy.
(laughs)
- Where's Daniel?
- Brian, we're not
putting out name on 04:43
That's your model, right?
- I'm really interested to
hear your ideas, Daniel,
on how we think we can
achieve the best performance.
- Definitely try to achieve
some form of consistent
level of speed first,
even at a lower pace,
so we kind of build momentum
and then we start to exploit
the straight lines but
keep that corner speed.
- Can we get a speed
trace out of the model?
- Absolutely.
- I would like to open
up a bit more at the corners
and be able to change a little
bit the way it's turning.
- I'm excited to reconvene
and see where we end up
on the leaderboard next week.
- Time to work.
(dance music)
- Been a pleasure, first lesson.
But am I satisfied?
No.
- Bye bye!
- Bye bye!
See you next week.
- See you guys.
Thanks for watching.
- Thanks for watching.
- Don't miss your chance
to place your time
to the leaderboard and
compete against me--
- And me, until the end of May.
- Check it out here.
(dance music)
Ooh, I like this.
- All right.
There's gonna be boxes on the track.
- It's kind of unfair, actually.
(laughs)
- We need to talk about a new
type of sensor.
- Stereo vision.
In order for us to perceive depth.
Two cameras, just like we have two eyes.
- I'm really looking forward to seeing
how our model does avoiding
objects on the track.
- Yeah this one has gone
to the grandstand look.
- It's for the fans!
You should not go out that much.
You will probably lose your job.
(laughs)
- Simple as keeping the
car in a straight line.
- That should be the easy bit.
- True.
- You or I could do that in
a FORMULA 1 car, Brian.
- I would love to try
that in a FORMULA 1 car.
- Well, I could.
(dance music)
