Instructor: Hello, my friends,
and welcome to this new data science use case with ChatGPT,
which this time will be on reinforcement learning.
So we're gonna tell ChatGPT right now
that we have programmed a virtual self-driving car
without any AI yet.
So we have just implemented it's ability to move forward,
turn left, turn right, and stop.
And we're gonna say that this self-driving car is rewarded
if it manages to reach a certain destination,
and penalized if it goes away from this destination,
or if it runs into some obstacles.
Okay, and then we're gonna ask ChatGPT
which AI model should be implemented
for the self-driving car and how.
Okay?
So let's do this.
I'm gonna open a new chat here.
And inside we're gonna say exactly,
"Hey, I have programmed
a virtual self-driving car
with the actions being
to move forward,
turn left,
turn right,
and stop.
And then this car is rewarded
once it reaches a certain destination,
and penalized
if it goes further away
from this destination,
or if it runs into some obstacles."
Okay, now, let's be greedier.
I just wanted to ask for some recommendations
on how to build a reinforcement learning model
for the self-driving car,
but let's actually ask to directly build it.
Why not?
Let's see what ChatGPT is capable of.
So I'm gonna ask, "Can you please
write me a Python code
using the best libraries
to build a reinforcement learning model
that implements
the AI inside that self-driving car?"
All right, so it's a bit greedy to ask this,
but we never know.
ChatGPT might give us exactly what we want.
So let's try and if it runs into any trouble,
we will help it.
Okay, so let's press Enter and here we go.
Sure, okay, that's a great start again.
"I can help you get started with implementing
a reinforcement learning model
for your self-driving car in Python."
Amazing.
"Here is some code that demonstrates how you can use,
OpenAI Gym library."
Awesome.
"To train a reinforcement learning model
for your self-driving car."
Okay, so that's amazing actually,
I didn't expect it to, ChatGPT to do it so easily,
and it makes all sense what it's doing now.
It's initializing, the Q-table,
the number of states, number of actions,
a learning rate initialized to 0.1,
a discount factor to 0.95.
This is really amazing, I'm in an awe.
The exploration rate to 0.5,
the maximum number of episodes to 1,000,
maximum number of steps per episodes to 100.
Then it trains the Q-learning model, amazing.
I'm gonna challenging him then to implement
a more advanced model like the deep Q-learning model,
or the A3C but let's see.
It implements everything from scratch with the For Loop.
It starts by choosing an action,
then it implements the step where the AI takes the action,
then it updates the Q-table and then it sets the state
to the next day.
This is really the reinforcement learning process,
the Q-learning process, this is amazing.
Then it even implements how to test the Q-learning model,
and then finally it closes the environment.
Wow, that was really amazing.
Okay, I didn't expect that much.
So since it did it so easily,
let's actually challenge it even more, even harder.
So first, let's say, of course,
"Thanks a lot, that was really helpful.
However,
I do know already
how to implement Q-learning.
So could you
please do the same
with a more
advanced reinforcement learning model like, for example,
deep Q-learning."
Or even better, let's be super greedy.
"A state of the art reinforcement learning model."
Okay, ready for this?
Here we go.
And, "Certainly."
He always says, "Certainly."
That's amazing.
"Here is some code that demonstrate
how you can use the OpenAI baselines library
to train a deep Q-learning model, DQN,
for your self-driving car."
And here we go again.
It gives us the code.
That's really amazing.
All right, so yes, it uses now the Wrap DeepMind library
from the baseline's common Atari library,
and it implements the AI again
with the deep Q-learning model this time.
That's really, really amazing.
"This code uses the DQN algorithm
to train a deep Q-learning model for your self-driving car.
The model will learn to take the best action at each state
in order to maximize the reward.
Alternatively, if you would like to use a more
advanced reinforcement learning model,
you can use the OpenAI Spinning Up library,
which provides implementations of state-of-the-art
reinforcement learning algorithms.
Here is some code that demonstrate how you can use
the Spinning Up library to train
a proximal policy optimization, PPO model."
Indeed, that's a great model.
"For your self-driving car."
And here you go, it gives you the model again
with the whole code.
Wow, what a revolution, this ChatGPT.
Seriously, I'm really impressed.
Right, and there you go, you have the code.
"This code uses the PPO algorithm
to train a reinforcement learning model
for your self-driving car.
The model will learn to take actions to maximize
the expected sum of discounted rewards.
I hope this helps.
Let me know if you have any questions,
or if you would like further assistance."
Well, even I didn't expect so much,
so I'm just impressed just like you,
the same time doing all this.
So we're gonna say, "A big thank you, that was amazing."
And done.
Thanks again for watching this data science use case
with ChatGPT.
Let's now move on to the next one.
And until then, enjoy machine learning.