Francois Chaubard

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744 appearances 1 recordings 1 series first heard Jul 2026 last heard 17 Jul

Francois Chaubard’s voice in public audio — every appearance, attributed to the second.

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Recordings per month over the last 12 months — 1 in all, peaking in Jul 2026 with 1.

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Y Combinator Startup Podcast · World Models, Explained · 17 Jul 2026 podcast

Exactly right.

And so if it works in us, it should work in robotics.

And I think that that takes us the rest of the distance.

Yeah, in classic RL, you'll have like, you know, if you do study Q learning, for example, you basically keep this matrix called the Q matrix.

And it's going to be s by a. And so I have this s by a states and actions.

And each one, I need, you know, some amount of counts of being in this state, action,

And I take the average value of taking that action in this state.

And that's my Q value there.

And it's a little bit more complicated than that.

There's Bellman equation, all this backup, all this stuff like that.

So this scales horribly because as the cardinality in my space gets bigger and my action space gets bigger, I become less and less sample efficient.

And so the classic trick, since I took C229 with Andrew Wong in 2012, is you do this.

Stick a neural network on it.

Exactly.

And you basically are just going to compress that state into some lower dimensional state space.

This actually predates deep learning.

We were doing stuff like this.

I think my first paper was basically doing something like this, basically turning a grid into a bunch of pyramids.

And the state was how much I'm in pyramid one or pyramid two or whatever.

But anyway, the neural network can just do this.

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