Name: 長期模型結果--用卡拉和Python自動駕駛汽車第6頁。 (Longer-term model results - Self-driving cars with Carla and Python p.6)
Uploaded: 2021-01-14T12:27:47.000Z
Duration: 20 min 26 s
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And welcome to another Carla video, possibly the last one.

So what I wanted to do is show you guys at least the best is a model that we've had, as well as just what what models look like after training for quite some time.

So so, as you can see, I mean this model.

This is just playing from one of the twitch streams.

Those videos will probably be viewable for awhile.

I'm not really sure when twitch removes the bad's.

But any waging, good twitch dot tv slash Centex and then go to videos and you can watch them in all their glory.

There's probably basically from the models beginning of training.

All the way up to this point has was all like streamed.

So anyway, you could definitely check it out.

It's it's decent at staying on a straight road.

It can take turns, not the greatest and the same thing.

Back when I was doing the GT A Siri's, although even here it's a little more impressive like this again.

It started off purely randomly, just taking random turns and straight and just random actions, and it at least learned something.

It is admittedly not very good at driving, but still, it has learned to do some things pretty good.

And I've seen it do some pretty cool maneuvers and all that.

And so I mean, it's something, but it's not.

It's not that great of a driver yet, but I mean, that's I I don't know what we should have expected, to be honest, Um, especially considering this is a de que en so dick.

Humans are really good at learning things, but they're slow at learning things, so yeah.

Anyway, let's talk about some of the findings that I, uh that we've made basically the things that we tried, what worked, what didn't work in, kind of like some of the theories as to why so, first of all, the model that you're looking at here, basically, this is the tensor board we trained for over 300,000 steps and by steps, I mean episodes, which translates to well over five days.

So So it took a long time, and that's not just five days on like a decent machine.

That's five days on the machine behind me.

That is the Lenovo P 9 20 workstations got to Artie X eight thousands in it.

It's got, like, 200 gigabytes of regular ram to see pews.

So five days on that on this machine, like in this machine's not weak.

Like my main usual machine is has a Titan or t X on it.

And it's still really just one runs, like one agent you could maybe stuff into.

So the other issue is, uh, you wanna have where we were kind of hoping for about 15 frames per second.

Ah, you wanna have high friends per second And ideally, we'd have even higher than 15.

At least with self driving car in grand theft auto, you know, really wanted more like 30 to 60 or more like the more friends per second you get, the just the better.

That agent is just simply gonna perform because making a couple of mistakes here and there just look, it's It's just a wash.

So anyway, unfortunately, that was as good as it got was 15 frames per second, but the model actually learned stuff.

So first of all, I'm scrolling through a bunch of stuff because it pride doesn't matter.

I mean, obviously, people think, Oh, that must matter.

I mean you want you definitely need the neural network toe.

Have some degree of accuracy because you want that model.

Um, it needs to sort of learn the Q values, but especially initially, the Q values are relatively worthless, right?

So especially initially, the accuracy is gonna be pretty weak.

And then over time, um, at least this one, it's almost like it didn't The model itself was pretty stagnant.

And keep in mind there's three options, so this is significantly above random, but it's still not like, super accurate or anything.

So anyway, uh, you know, we later decided, Hey, let's try a larger model.

All nor network changed from Exception, which the main issue, with exception going to the text based version.

That's tutorial was the following this this loss.

So the loss just continued to explode on us and in general of loss is exploding.

It's super rare that the model is going to recover from a loss explosion.

And even if it does, it's just probably gonna happen again.

Explosion signals Something is fundamentally wrong Now.

Um, Daniel claims to have a model that's pretty good that also exhibits and has just constant loss explosions says it works Well, we'll see if we find that we have a model that actually works well despite having lost explosion, I'll be sure to let you know.

But for the most part wanted to move away from exception.

And ah, at least the reason why we assumed that was probably a problem is mainly exception has basically the amount of parameters that you can actually train in tweak as a neural network, all basically 23 million, Uh, and then er, actually, yeah, yeah, 23 million pretty much, whereas a 64 by three continents three million.

So it's just a much simpler problem for the agent for the neural network just to kind of try to figure out.

Yes, we're using a neural network, but it's kind of like the neural network is more so there to help us generalize que values right?

It's not really a super complex task of the neural network has to do so anyway, it seems like maybe exception was overkill.

But then it seems like a 64 by three was maybe not large enough of a model because our accuracy just never became very good.

So in general with neural networks, if you're trying to get good accuracy, the goal is often make.

The network is small as you can while it's still learns.

Once you get to a point where you've made the network too small and it doesn't quite learn, make it just slightly bigger again.

We did try like a 60 are like doing something like 64 64 1 28 and then, like maybe making four layers of 64 adding yet we tried a bunch of different combinations, and honestly, nothing was better in this model on and again, I understand this model isn't look the greatest, but it does Lane keep.

Has learned, like straights and sort of how to get back on track and stuff like that.

And also in at least what we're looking at, I don't know.

We set the minimum absalon to be 10% basically.

So, still, 10% of the time these agents do some random action, but anyway, this is, I think, when I was streaming in a pre sure, we always just put it to 10%.

Just so it was mostly what that agent was actually going to do if it was left to its own devices.

So anyways, okay, so, uh, continuing on let me scroll past this is just like weather patterns and stuff that we decided to graph as we change them.

Just because the longer the agent is surviving in the episode, that's useful information.

Also, that would kind of tell me like especially if episode average time was approaching like 10 seconds, which was the limit per episode that might suggest maybe we should boost that time.

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長期模型結果--用卡拉和Python自動駕駛汽車第6頁。 (Longer-term model results - Self-driving cars with Carla and Python p.6)

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