Dear Fellow Scholars, this is Two Minute Papers with Károly Zsolnai-Fehér.
In this project, OpenAI built a hide and seek game for their AI agents to play.
While we look at the exact rules here, I will note that the goal of the project was to pit
two AI teams against each other, and hopefully see some interesting emergent behaviors.
And, boy, did they do some crazy stuff.
The coolest part is that the two teams compete against each other, and whenever one team
discovers a new strategy, the other one has to adapt.
Kind of like an arms race situation, and it also resembles generative adversarial network
a little.
And the results are magnificent, amusing, weird - you’ll see in a moment.
These agents learn from previous experiences, and to the surprise of no one, for the first
few million rounds, we start out with…pandemonium.
Everyone just running around aimlessly.
Without proper strategy and semi-random movements, the seekers are favored and hence win the
majority of the games.
Nothing to see here.
Then, over time, the hiders learned to lock out the seekers by blocking the doors off
with these boxes and started winning consistently.
I think the coolest part about this is that the map was deliberately designed by the OpenAI
scientists in a way that the hiders can only succeed through collaboration.
They cannot win alone and hence, they are forced to learn to work together.
Which they did, quite well.
But then, something happened.
Did you notice this pointy, doorstop-shaped object?
Are you thinking what I am thinking?
Well, probably, and not only that, but about 10 million rounds later, the AI also discovered
that it can be pushed near a wall and be used as a ramp, and, tadaa!
Got’em!
Te seekers started winning more again.
So, the ball is now back on the court of the hiders.
Can you defend this?
If so, how?
Well, these resourceful little critters learned that since there is a little time at the start
of the game when the seekers are frozen, apparently, during this time, they cannot see them, so
why not just sneak out and steal the ramp, and lock it away from them.
Absolutely incredible.
Look at those happy eyes as they are carrying that ramp.
And, you think it all ends here?
No, no, no.
Not even close.
It gets weirder.
Much weirder.
When playing a different map, a seeker has noticed that it can use a ramp to climb on
the top of a box, and, this happens.
Do you think couchsurfing is cool?
Give me a break!
This is box surfing!
And, the scientists were quite surprised by this move as this was one of the first cases
where the seeker AI seems to have broken the game.
What happens here is that the physics system is coded in a way that they are able to move
around by exerting force on themselves, but, there is no additional check whether they
are on the floor or not, because who in their right mind would think about that?
As a result, something that shouldn’t ever happen does happen here.
And, we’re still not done yet, this paper just keeps on giving.
A few hundred million rounds later, the hiders learned to separate all the ramps from the
boxes.
Dear Fellow Scholars, this is proper box surfing defense…then, lock down the remaining tools
and build a shelter.
Note how well rehearsed and executed this strategy is - there is not a second of time
left until the seekers take off.
I also love this cheeky move where they set up the shelter right next to the seekers,
and I almost feel like they are saying “yeah see this here?
there is not a single thing you can do about it”.
In a few isolated cases, other interesting behaviors also emerged, for instance, the
hiders learned to exploit the physics system and just chuck the ramp away.
After that, the seekers go “what?”
“what just happened?”.
But don’t despair, and at this point, I would also recommend that you hold on to your
papers because there was also a crazy case where a seeker also learned to abuse a similar
physics issue and launch itself exactly onto the top of the hiders.
Man, what a paper.
This system can be extended and modded for many other tasks too, so expect to see more
of these fun experiments in the future.
We get to do this for a living, and we are even being paid for this.
I can’t believe it.
In this series, my mission is to showcase beautiful works that light a fire in people.
And this is, no doubt, one of those works.
Great idea, interesting, unexpected results, crisp presentation.
Bravo OpenAI!
Love it.
So, did you enjoy this?
What do you think?
Make sure to leave a comment below.
Also, if you look at the paper, it contains comparisons to an earlier work we covered
about intrinsic motivation, shows how to implement circular convolutions for the agents to detect
their environment around them, and more.
Thanks for watching and for your generous support, and I'll see you next time!