Sergey Levine: Robotics and Machine Learning
嘉宾：Sergey Levine
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The following is a conversation with Sergei Levine,
a professor at Berkeley and a world-class researcher
in deep learning, reinforcement learning,
robotics and computer vision,
including the development of algorithms
for end-to-end training of neural network policies
that combine perception and control,
scalable algorithms for inverse reinforcement learning
and in general, deep RL algorithms.
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And now here's my conversation with Sergei Levine.
What's the difference between a state of the art human,
such as you and I,
well, I don't know if we qualify as state of the art humans,
but a state of the art human and a state of the art robot?
- That's a very interesting question.
Robot capability is, it's kind of a,
I think it's a very tricky thing to understand
because there are some things that are difficult
that we wouldn't think are difficult
and some things that are easy
that we wouldn't think are easy.
And there's also a really big gap
between capabilities of robots in terms of hardware
and their physical capability
and capabilities of robots
in terms of what they can do autonomously.
There is a little video that I think robotics researchers
really like to show,
special robotics learning researchers like myself
from 2004 from Stanford,
which demonstrates a prototype robot called the PR1.
And the PR1 was a robot that was designed
as a home assistance robot.
And there's this beautiful video showing the PR1
tidying up a living room, putting away toys,
and at the end, bringing a beer
to the person sitting on the couch,
which looks really amazing.
And then the punchline is that this robot
is entirely controlled by a person.
So you can, so in some ways,
the gap between a state-of-the-art human
and a state-of-the-art robot,
if the robot has a human brain,
is actually not that large.
Now, obviously, like human bodies are sophisticated
and very robust and resilient in many ways,
but on the whole, if we're willing to like spend
a bit of money and do a bit of engineering,
we can kind of close the hardware gap almost.
But the intelligence gap, that one is very wide.
- And when you say hardware,
you're referring to the physical sort of the actuators,
the actual body of the robot
as opposed to the hardware on which the cognition,
the hardware of the nervous system.
- Yes, exactly.
I'm referring to the body rather than the mind.
So that means that kind of the work is cut out for us.
Like while we can still make the body better,
we kind of know that the big bottleneck right now
is really the mind.
- And how big is that gap?
How big is the difference in your sense
of ability to learn, ability to reason,
ability to perceive the world
between humans and our best robots?
- The gap is very large and the gap becomes larger
the more unexpected events can happen in the world.
So essentially the spectrum along which you can measure
the size of that gap is the spectrum
of how open the world is.
If you control everything in the world very tightly,
if you put the robot in like a factory
and you tell it where everything is
and you rigidly program its motion,
then it can do things.
One might even say in a superhuman way.
It can move faster, it's stronger,
it can lift up a car and things like that.
But as soon as anything starts to vary in the environment,
now it'll trip up and if many, many things vary
like they would like in your kitchen, for example,
then things are pretty much like wide open.
- Now again, we're gonna stick a bit
on the philosophical questions,
but how much on the human side of the cognitive abilities
in your sense is nature versus nurture?
So how much of it is a product of evolution
and how much of it is something we'll learn
from sort of scratch from the day we're born?
- I'm gonna read into your question
as asking about the implications of this for AI.
- Of course, exactly.
- I'm not a biologist,
I can't really speak authoritatively about it.
- So in Tlingit, if it's all about learning,
then there's more hope for AI.
So the way that I look at this is that,
well, first of course, biology is very messy.
And if you ask the question, how does a person do something
or how does a person's mind do something,
you can come up with a bunch of hypotheses
and oftentimes you can find support for many different,
often conflicting hypotheses.
One way that we can approach the question
of what the implications of this for AI are
is we can think about what's sufficient.
So maybe a person is from birth very, very good
at some things like, for example, recognizing faces.
There's a very strong evolutionary pressure to do that.
If you can recognize your mother's face,
then you're more likely to survive
and therefore people are good at this.
But we can also ask like,
what's the minimum sufficient thing?
And one of the ways that we can study
the minimal sufficient thing is we could, for example,
see what people do in unusual situations.
If you present them with things
that evolution couldn't have prepared them for.
Our daily lives actually do this to us all the time.
We didn't evolve to deal with automobiles
and space flight and whatever.
So there are all these situations
that we can find ourselves in and we do very well there.
Like I can give you a joystick to control a robotic arm,
which you've never used before
and you might be pretty bad for the first couple of seconds.
But if I tell you like, your life depends
on using this robotic arm to like open this door,
you'll probably manage it.
Even though you've never seen this device before,
you've never used the joystick control,
and you'll kind of muddle through it.
And that's not your evolved natural ability,
that's your flexibility, your adaptability.
And that's exactly where our current robotic systems
really kind of fall flat.
- But I wonder how much general,
almost what we think of as common sense,
pre-trained models underneath all of that.
So that ability to adapt to a joystick
requires you to have a kind of,
§02Transcript · 08:31–16:36
you know, I'm human, so it's hard for me
to introspect all the knowledge I have about the world.
But it seems like there might be an iceberg underneath
of the amount of knowledge we actually bring to the table.
That's kind of the open question.
- I think there's absolutely an iceberg of knowledge
that we bring to the table,
but I think it's very likely that iceberg of knowledge
is actually built up over our lifetimes.
Because we have a lot of prior experience to draw on
and it kind of makes sense that the right way
for us to optimize our efficiency,
our evolutionary fitness and so on,
is to utilize all of that experience
to build up the best iceberg we can get.
And that's actually one of,
while that sounds an awful lot
like what machine learning actually does,
I think that for modern machine learning,
it's actually a really big challenge
to take this unstructured massive experience
and distill out something that looks
like a common sense understanding of the world.
And perhaps part of that is,
it's not because something about machine learning itself
is broken or hard,
but because we've been a little too rigid
in subscribing to a very supervised,
very rigid notion of learning.
Kind of the input output X's go to Y's sort of model.
And maybe what we really need to do
is to view the world more as like a massive experience
that is not necessarily providing any rigid supervision,
but sort of providing many, many instances
of things that could be.
And then you take that and you distill it
into some sort of common sense understanding.
- I see.
Well, you're painting an optimistic, beautiful picture,
especially from the robotics perspective,
'cause that means we just need to invest
and build better learning algorithms,
figure out how we can get access to more and more data
for those learning algorithms to extract signal from,
and then accumulate that iceberg of knowledge.
It's a beautiful picture.
It's a hopeful one.
- I think it's potentially a little bit more
than just that.
And this is where we perhaps reach the limits
of our current understanding.
But one thing that I think that the research community
hasn't really resolved in a satisfactory way
is how much it matters where that experience comes from.
Like, do you just like download everything on the internet
and cram it into essentially the 21st century analog
of the giant language model and then see what happens?
Or does it actually matter whether your machine
physically experiences the world,
or in the sense that it actually attempts things,
observes the outcome of its actions,
and kind of augments its experience that way?
- That it chooses which parts of the world
it gets to interact with and observe and learn from.
- Right, it may be that the world is so complex
that simply obtaining a large mass of sort of IID samples
of the world is a very difficult way to go.
But if you are actually interacting with the world
and essentially performing this sort of hard negative mining
by attempting what you think might work,
observing the sometimes happy
and sometimes sad outcomes of that,
and augmenting your understanding using that experience,
and you're just doing this continually for many years,
maybe that sort of data in some sense
is actually much more favorable
to obtaining a common sense understanding.
One reason we might think that this is true
is that what we associate with common sense
or lack of common sense is often characterized
by the ability to reason
about kind of counterfactual questions.
Like if I were to,
here I'm this bottle of water sitting on the table,
everything is fine if I were to knock it over,
which I'm not gonna do,
but if I were to do that, what would happen?
And I know that nothing good would happen from that,
but if I have a bad understanding of the world,
I might think that that's a good way for me
to like gain more utility.
If I actually go about my daily life doing the things
that my current understanding of the world suggests
will give me high utility,
in some ways I'll get exactly the right supervision
to tell me not to do those bad things
and to keep doing the good things.
- So there's a spectrum between IID,
random walk through the space of data,
and then there's, and what we humans do.
Well, I don't even know if we do it optimal,
but there might be beyond.
So this open question that you raised,
where do you think systems,
intelligent systems that would be able
to deal with this world fall?
Can we do pretty well by reading all of Wikipedia,
sort of randomly sampling it like language models do,
or do we have to be exceptionally selective
and intelligent about which aspects
of the world we interact with?
- So I think this is first an open scientific problem,
and I don't have like a clear answer,
but I can speculate a little bit.
And what I would speculate is that
you don't need to be super, super careful.
I think it's less about like being careful
to avoid the useless stuff,
and more about making sure that you hit
on the really important stuff.
So perhaps it's okay if you spend part of your day
just guided by your curiosity,
visiting interesting regions of your state space,
but it's important for you to,
every once in a while,
make sure that you really try out the solutions
that your current model of the world suggests
might be effective, and observe whether those solutions
are working as you expect or not.
And perhaps some of that is really essential
to have kind of a perpetual improvement loop.
Like this perpetual improvement loop is really like,
that's really the key,
the key that's gonna potentially distinguish
the best current methods from the best methods
of tomorrow in a sense.
- How important do you think is exploration
or total out of the box thinking,
exploration in this space,
is you jump to totally different domains.
So you kind of mentioned there's an optimization problem,
you kind of explore the specifics of a particular strategy,
whatever the thing you're trying to solve.
How important is it to explore totally outside
of the strategies that have been working for you so far?
What's your intuition there?
- Yeah, I think it's a very problem dependent
kind of question.
And I think that that's actually,
in some ways that question gets at
one of the big differences between
sort of the classic formulation
of a reinforcement learning problem
and some of the sort of more open-ended reformulations
of that problem that have been explored in recent years.
So classically, reinforcement learning
is framed as a problem of maximizing utility,
like any kind of rational AI agent.
And then anything you do is in service
to maximizing that utility.
But a very interesting kind of way to look at,
and I'm not necessarily saying
this is the best way to look at it,
but an interesting alternative way
to look at these problems is as something
where you first get to explore the world,
however you please, and then afterwards,
you will be tasked with doing something.
And that might suggest a somewhat different solution.
So if you don't know what you're gonna be tasked with doing
and you just wanna prepare yourself optimally
for whatever your uncertain future holds,
maybe then you will choose to attain some sort of coverage,
build up sort of an arsenal of cognitive tools, if you will,
such that later on when someone tells you,
now your job is to fetch the coffee for me,
you will be well-prepared to undertake that task.
- And you see that as the modern formulation
of the reinforcement learning problem,
as the kind of, the more multitask,
the general intelligence kind of formulation.
- I think that's one possible vision
of where things might be headed.
I don't think that's by any means the mainstream
or standard way of doing things,
and it's not like if I had to--
- But I like it.
It's a beautiful vision.
So maybe actually take a step back.
What is the goal of robotics?
What's the general problem of robotics
we're trying to solve?
You actually kind of painted two pictures here,
one of sort of the narrow, one of the general.
What, in your view, is the big problem of robotics?
Again, ridiculously philosophical, high-level questions.
- I think that, you know, maybe there are two ways
I can answer this question.
One is there's a very pragmatic problem,
which is like what would make robots,
§03Transcript · 16:40–25:08
what would sort of maximize the usefulness of robots?
And there the answer might be something like a system
where a system that can perform whatever task
a human user sets for it, you know,
within the physical constraints, of course.
If you tell it to teleport to another planet,
it probably can't do that.
But if you ask it to do something
that's within its physical capability,
then potentially with a little bit of additional training
or a little bit of additional trial and error,
it ought to be able to figure it out
in much the same way as like a human teleoperator
ought to figure out how to drive the robot to do that.
That's kind of the very pragmatic view
of what it would take to kind of solve
the robotics problem, if you will.
But I think that there is a second answer,
and that answer is a lot closer
to why I want to work on robotics,
which is that I think it's less about
what it would take to do a really good job
in the world of robotics, but more the other way around,
what robotics can bring to the table
to help us understand artificial intelligence.
- So your dream fundamentally is to understand intelligence.
- Yes, I think that's the dream for many people
who actually work in this space.
I think that there's something very pragmatic
and very useful about studying robotics,
but I do think that a lot of people that go into this field,
actually, you know, the things that they draw inspiration
from are the potential for robots
to help us learn about intelligence and about ourselves.
- So that's fascinating, that robotics is basically
the space by which you can get closer to understanding
the fundamentals of artificial intelligence.
So what is it about robotics that's different
from some of the other approaches?
So if we look at some of the early breakthroughs
in deep learning or in the computer vision space
and the natural language processing,
there's really nice, clean benchmarks
that a lot of people competed on
and thereby came up with a lot of brilliant ideas.
What's the fundamental difference to you
between computer vision, purely defined an image net
and kind of the bigger robotics problem?
- So there are a couple of things.
One is that with robotics,
you kind of have to take away many of the crutches.
So you have to deal with both the particular problems
of perception, control, and so on,
but you also have to deal with the integration
of those things.
And classically, we've always thought of the integration
as kind of a separate problem.
So a classic kind of modular engineering approach
is that we solve the individual sub problems
and wire them together, and then the whole thing works.
And one of the things that we've been seeing
over the last couple of decades is that,
well, maybe studying the thing as a whole
might lead to just like very different solutions
than if we were to study the parts and wire them together.
So the integrative nature of robotics research
helps us see the different perspectives on the problem.
Another part of the answer is that with robotics,
it casts a certain paradox into very clever relief.
So this is sometimes referred to as a Moravic's paradox,
the idea that in artificial intelligence,
things that are very hard for people
can be very easy for machines and vice versa.
Things that are very easy for people
can be very hard for machines.
So, you know, integral and differential calculus
is pretty difficult to learn for people,
but if you program a computer to do it,
it can derive derivatives and integrals for you
all day long without any trouble.
Whereas some things like, you know,
drinking from a cup of water,
very easy for a person to do,
very hard for a robot to deal with.
And sometimes when we see such blatant discrepancies,
that gives us a really strong hint
that we're missing something important.
So if we really try to zero in on those discrepancies,
we might find that little bit that we're missing.
And it's not that we need to make machines better
or worse at math and better at drinking water,
but just that by studying those discrepancies,
we might find some new insight.
- So that could be in any space.
It doesn't have to be robotics,
but you're saying, I mean,
I get it's kind of interesting that robotics
seems to have a lot of those discrepancies.
So the Hans-Marx paradox is probably referring
to the space of the physical interaction,
like you said, object manipulation, walking,
all the kind of stuff we do in the physical world.
How do you make sense,
if you were to try to disentangle the Marvox paradox,
like why is there such a gap in our intuition about it?
Why do you think manipulating objects is so hard
from everything you've learned
from applying reinforcement learning in this space?
- Yeah, I think that one reason is maybe that
for many of the other problems that we've studied
in AI and computer science and so on,
the notion of input, output and supervision
is much, much cleaner.
So computer vision, for example,
deals with very complex inputs,
but it's comparatively a bit easier,
at least up to some level of abstraction
to cast it as a very tightly supervised problem.
It's comparatively much, much harder
to cast robotic manipulation
as a very tightly supervised problem.
You can do it, it just doesn't seem to work all that well.
So you could say that, well,
maybe we get a label data set
where we know exactly which motor commands to send
and then we train on that.
But for various reasons,
that's not actually like such a great solution.
And it also doesn't seem to be even remotely similar
to how people and animals learn to do things
because we're not told by like our parents,
here's how you fire your muscles in order to walk.
We do get some guidance,
but the really low level detailed stuff,
we figure out mostly on our own.
- And that's what you mean by tightly coupled,
that every single little sub action
gets a supervised signal of whether it's a good one or not.
- Right.
So while in computer vision,
you could sort of imagine up to a level of abstraction
that maybe somebody told you this is a car
and this is a cat and this is a dog,
in motor control, it's very clear
that that was not the case.
- If we look at sort of the sub spaces of robotics,
that again, as you said,
robotics integrates all of them together
and we get to see how this beautiful mess interplays.
So there's nevertheless still perception.
So it's the computer vision problem,
broadly speaking, understanding the environment.
Then there's also, maybe you can correct me
on this kind of categorization of the space.
Then there's prediction in trying to anticipate
what things are going to do into the future
in order for you to be able to act in that world.
And then there's also this game theoretic aspect
of how your actions will change the behavior of others.
In this kind of space, what,
and this is bigger than reinforcement learning,
this is just broadly looking at the problem in robotics.
What's the hardest problem here?
Or is there, or is what you said true
that when you start to look at all of them together,
that's a whole nother thing?
Like you can't even say which one individually is harder
because all of them together,
you should only be looking at them all together.
- I think when you look at them all together,
some things actually become easier.
And I think that's actually pretty important.
So we had, back in 2014, we had some work,
basically our first work on end-to-end
reinforcement learning for robotic manipulation skills
from vision, which at the time was something
that seemed a little inflammatory and controversial
in the robotics world.
But other than the inflammatory
and controversial part of it,
the point that we were actually trying to make in that work
is that for the particular case
of combining perception and control,
you could actually do better if you treat them together
than if you try to separate them.
And the way that we tried to demonstrate this
is we picked a fairly simple motor control task
where a robot had to insert a little red trapezoid
into a trapezoidal hole.
And we had our separated solution,
which involved first detecting the hole
using a pose detector,
and then actuating the arm to put it in.
And then our intent solution,
which just mapped pixels to the torques.
And one of the things we observed is that
if you use the intent solution,
essentially the pressure on the perception part
of the model is actually lower.
Like it doesn't have to figure out exactly
§04Transcript · 25:09–32:54
where the thing is in 3D space.
It just needs to figure out where it is,
distributing the errors in such a way
that the horizontal difference matters
more than the vertical difference,
because vertically it just pushes it down all the way
until it can't go any further.
And their perceptual errors are a lot less harmful,
whereas perpendicular to the direction of motion,
perceptual errors are much more harmful.
So the point is that if you combine these two things,
you can trade off errors between the components
optimally to best accomplish the task.
And the components can actually be weaker
while still leading to better overall performance.
- That's a profound idea.
I mean, in the space of pegs and things like that,
it's quite simple.
It almost is tempting to overlook,
but that seems to be at least intuitively an idea
that should generalize to basically all aspects
of perception control.
- Of course.
- That one strengthens the other.
- Yeah, and people who have studied
sort of perceptual heuristics in humans and animals
find things like that all the time.
So one very well-known example
of this is something called the gaze heuristic,
which is a little trick that you can use
to intercept a flying object.
So if you want to catch a ball, for instance,
you could try to localize it in 3D space,
estimate its velocity,
estimate the effect of wind resistance,
solve a complex system of differential equations
in your head,
or you can maintain a running speed
so that the object stays in the same position
as in your field of view.
So if it dips a little bit, you speed up.
If it rises a little bit, you slow down.
And if you follow the simple rule,
you'll actually arrive at exactly the place
where the object lands and you'll catch it.
And humans use it when they play baseball.
Human pilots use it when they fly airplanes
to figure out if they're about to collide with somebody.
Frogs use this to catch insects and so on and so on.
So this is something that actually happens in nature.
And I'm sure this is just one instance of it
that we were able to identify
just because all the scientists were able to identify
because it's so prevalent,
but there are probably many others.
- Do you have a, just so we can zoom in
as we talk about robotics,
do you have a canonical problem,
sort of a simple, clean, beautiful,
representative problem in robotics
that you think about
when you're thinking about some of these problems?
We talked about robotic manipulation.
To me, that seems intuitively,
at least the robotics community has converged towards that
as a space that's the canonical problem.
If you agree, then maybe do you zoom in
in some particular aspect of that problem
that you just like?
Like if we solve that problem perfectly,
it'll unlock a major step
towards human-level intelligence.
- I don't think I have like a really great answer
to that.
And I think partly the reason I don't have a great answer
kind of has to do with the,
it has to do with the fact that the difficulty
is really in the flexibility and adaptability
rather than in doing a particular thing really, really well.
So it's hard to just say like,
oh, if you can, I don't know,
like shuffle a deck of cards as fast
as like a Vegas casino dealer,
then you'll be very proficient.
It's really the ability to quickly figure out
how to do some arbitrary new thing well enough
to like, you know, to move on to the next arbitrary thing.
- But the source of newness and uncertainty,
have you found problems in which it's easy
to generate new newness-ness-nesses?
- Yeah.
- New types of newness.
- Yeah.
So a few years ago,
so if you had asked me this question around like 2016,
maybe I would have probably said that robotic grasping
is a really great example of that
because it's a task with great real world utility.
Like you will get a lot of money if you can do it well.
- What is robotic grasping?
- Picking up any object.
- With a robotic hand.
- Exactly.
So you will get a lot of money if you do it well
because lots of people want to run warehouses with robots.
And it's highly non-trivial
because very different objects
will require very different grasping strategies.
But actually since then,
people have gotten really good at building systems
to solve this problem.
It's to the point where I'm not actually sure
how much more progress we can make
with that as like the main guiding thing.
But it's kind of interesting to see the kind of methods
that have actually worked well in that space
because a robotic grasping classically
used to be regarded very much as
kind of almost like a geometry problem.
So people who have studied the history of computer vision
will find this very familiar
that it's kind of in the same way
that in the early days of computer vision,
people thought of it very much
as like an inverse graphics thing.
In robotic grasping,
people thought of it as an inverse physics problem.
Essentially, you look at what's in front of you,
figure out the shapes,
then use your best estimate of the laws of physics
to figure out where to put your fingers
and then you pick up the thing.
And it turns out that what works really well
for robotic grasping,
instantiated in many different recent works,
including our own, but also ones from many other labs,
is to use learning methods
with some combination of either exhaustive simulation
or like actual real world trial and error.
And it turns out that those things
actually work really well
and then you don't have to worry about
solving geometry problems or physics problems.
- So what are, just by the way, in the grasping,
what are the difficulties that have been worked on?
So one is like the materials of things,
maybe occlusions and the perception side.
Why is it such a difficult,
why is picking stuff up such a difficult problem?
- Yeah, it's a difficult problem
because the number of things
that you might have to deal with
or the variety of things that you have to deal with
is extremely large.
And oftentimes things that work for one class of objects
won't work for other classes of objects.
So if you get really good at picking up boxes
and now you have to pick up plastic bags,
you just need to employ a very different strategy.
And there are many properties of objects
that are more than just their geometry.
It has to do with the bits that are easier to pick up,
the bits that are harder to pick up,
the bits that are more flexible,
the bits that will cause the thing to pivot and bend
and drop out of your hand
versus the bits that result in a nice secure grasp,
things that are flexible,
things that if you pick them up the wrong way,
they'll fall upside down and the contents will spill out.
So there's all these little details that come up,
but the task is still kind of,
can be characterized as one task.
Like there's a very clear notion of
you did it or you didn't do it.
- So in terms of spilling things,
there creeps in this notion that starts to sound
and feel like common sense reasoning.
Do you think solving the general problem of robotics
requires common sense reasoning,
requires general intelligence,
this kind of human level capability of,
like you said, be robust and deal with uncertainty,
but also be able to sort of reason
and assimilate different pieces of knowledge that you have?
Yeah.
What are your thoughts on the needs
of common sense reasoning in the space
of the general robotics problem?
- So I'm gonna slightly dodge that question
and say that I think maybe actually
it's the other way around is that studying robotics
can help us understand how to put common sense
into our AI systems.
One way to think about common sense is that,
and why our current systems might lack common sense,
is that common sense is a property,
is an emergent property of actually having to interact
with a particular world, a particular universe,
and get things done in that universe.
§05Transcript · 32:56–41:09
So you might think that, for instance,
like an image captioning system,
maybe it looks at pictures of the world
and it types out English sentences.
So it kind of deals with our world.
And then you can easily construct situations
where image captioning systems do things
that defy common sense,
like give it a picture of a person wearing a fur coat
and we'll say it's a teddy bear.
But I think what's really happening in those settings
is that the system doesn't actually live in our world,
it lives in its own world that consists of pixels
and English sentences,
and doesn't actually consist of like,
having to put on a fur coat in the winter
so you don't get cold.
So perhaps the reason for the disconnect
is that the systems that we have now
simply inhabit a different universe.
And if we build AI systems that are forced to deal
with all of the messiness and complexity of our universe,
maybe they will have to acquire common sense
to essentially maximize their utility.
Whereas the systems we're building now
don't have to do that, they can take some shortcut.
- That's fascinating.
You've a couple times already sort of reframed
the role of robotics in this whole thing.
And for some reason,
I don't know if my way of thinking is common,
but I thought like,
we need to understand and solve intelligence
in order to solve robotics.
And you're kind of framing it as,
no, robotics is one of the best ways
to just study artificial intelligence
and build sort of like,
robotics is like the right space
in which you get to explore
some of the fundamental learning mechanisms,
fundamental sort of multimodal, multitask,
aggregation of knowledge mechanisms
that are required for general intelligence.
That's really interesting way to think about it.
But let me ask about learning.
Can the general sort of robotics,
the epitome of the robotics problem
be solved purely through learning,
perhaps end-to-end learning?
Sort of learning from scratch
as opposed to injecting human expertise
and rules and heuristics and so on?
- I think that in terms of the spirit of the question,
I would say yes.
I mean, I think that though in some ways
it's maybe like an overly sharp dichotomy.
Like, I think that in some ways when we build algorithms,
at some point a person does something.
- Yeah, hyper parameters, there's always--
- A person turned on the computer,
a person implemented TensorFlow.
But yeah, I think that in terms of the point
that you're getting at, I do think the answer is yes.
I think that we can solve many problems
that have previously required meticulous manual engineering
through automated optimization techniques.
And actually one thing I will say on this topic is
I don't think this is actually a very radical
or very new idea.
I think people have been thinking
about automated optimization techniques
as a way to do control for a very, very long time.
And in some ways what's changed is really more the name.
So today we would say that, oh, my robot does
machine learning, it does reinforcement learning.
Maybe in the 1960s you'd say,
oh, my robot is doing optimal control.
And maybe the difference between typing out
a system of differential equations
and doing feedback linearization
versus training a neural net,
maybe it's not such a large difference.
It's just pushing the optimization deeper
and deeper into the thing.
- Well, it's interesting you think that way,
but with, especially with deep learning,
that the accumulation of sort of experiences
in data form to form deep representations
starts to feel like knowledge
as opposed to optimal control.
So this feels like there's an accumulation of knowledge
through the learning process.
- Yes, yeah.
So I think that is a good point,
that one big difference between learning-based systems
and classic optimal control systems
is that learning-based systems in principle
should get better and better the more they do something.
And I do think that that's actually
a very, very powerful difference.
- So if we look back at the world of expert systems
and symbolic AI and so on,
of using logic to accumulate expertise,
human expertise, human encoded expertise,
do you think that will have a role at some point?
Deep learning, machine learning, reinforcement learning
has shown incredible results and breakthroughs
and just inspired thousands, maybe millions of researchers.
But there's this less popular now,
but it used to be popular idea of symbolic AI.
Do you think that will have a role?
- I think in some ways,
the kind of the descendants of symbolic AI
actually already have a role.
So this is the highly biased history from my perspective.
You say that, well, initially we thought
that rational decision-making
involves logical manipulation.
So you have some model of the world
expressed in terms of logic.
You have some query,
like what action do I take in order for X to be true?
And then you manipulate
your logical symbolic representation to get an answer.
What that turned into somewhere in the 1990s is,
well, instead of building kind of predicates
and statements that have true or false values,
we'll build probabilistic systems
where things have probabilities associated
and probabilities of being true and false.
And that turned into Bayes nets.
And that provided sort of a boost
to what were really,
still essentially logical inference systems,
just probabilistic logical inference systems.
And then people said, well, let's actually learn
the individual probabilities inside these models.
And then people said, well,
let's not even specify the nodes in the models.
Let's just put a big neural net in there.
But in many ways,
I see these as actually kind of descendants
from the same idea.
It's essentially instantiating rational decision-making
by means of some inference process
and learning by means of an optimization process.
So in a sense, I would say, yes, it has a place.
And in many ways, that place is,
it already holds that place.
- It's already in there.
Yeah, it's just by different,
it looks slightly different than it was before.
- Yeah, but there are some things
that we can think about
that make this a little bit more obvious.
Like if I train a big neural net model
to predict what will happen
in response to my robot's actions,
and then I run probabilistic inference,
meaning I invert that model
to figure out the actions
that lead to some plausible outcome.
Like to me, that seems like a kind of logic.
You have a model of the world
that just happens to be expressed by a neural net,
and you are doing some inference procedure,
some sort of manipulation on that model
to figure out the answer to a query that you have.
- It's the interpretability,
it's the explainability though
that seems to be lacking more so.
Because the nice thing about expert systems
is you can follow the reasoning of the system.
That to us mere humans is somehow compelling.
It's just, I don't know what to make of this fact
that there's a human desire for intelligent systems
to be able to convey in a poetic way to us
why it made the decisions it did.
Like tell a convincing story.
And perhaps that's like a silly human thing.
Like we shouldn't expect that of intelligent systems.
Like we should be super happy
that there is intelligent systems out there.
But if I were to sort of psychoanalyze the researchers
at the time, I would say expert systems
connected to that part.
That desire of AI researchers
for systems to be explainable.
I mean, maybe on that topic,
do you have a hope that sort of inferences
of learning-based systems will be as explainable
as the dream was with expert systems, for example?
- I think it's a very complicated question
because I think that in some ways
the question of explainability
is kind of very closely tied
to the question of like performance.
Like, why do you want your system to explain itself?
Well, so that when it screws up,
§06Transcript · 41:11–49:29
you can kind of figure out why it did it.
- Right.
- But in some ways that's a much bigger problem, actually.
Like your system might screw up
and then it might screw up in how it explains itself.
Or you might have some bug somewhere
so that it's not actually doing what it was supposed to do.
So, maybe a good way to view that problem
is really as a bigger problem of verification and validation
of which explainability is sort of one component.
- I see.
I just see it differently.
I see explainability, you put it beautifully.
I think you actually summarize the field of explainability.
But to me, there's another aspect of explainability
which is like storytelling
that has nothing to do with errors or with like,
it uses errors as elements of its story
as opposed to a fundamental need
to be explainable when errors occur.
It's just that for other intelligence systems
to be in our world,
we seem to want to tell each other stories.
And that's true in the political world,
that's true in the academic world.
And that, you know,
neural networks are less capable of doing that.
Or perhaps they're equally capable
of storytelling and storytelling.
Maybe it doesn't matter
what the fundamentals of the system are.
You just need to be a good storyteller.
Maybe one specific story I can tell you about
in that space is actually about some work
that was done by my former collaborator
who's now a professor at MIT named Jacob Andreas.
Jacob actually works in natural language processing,
but he had this idea to do a little bit of work
in reinforcement learning
and how natural language can basically structure
the internals of policies trained with RL.
And one of the things he did is he set up a model
that attempts to perform some tasks
that's defined by a reward function,
but the model reads in a natural language instruction.
So this is a pretty common thing to do
in instruction following.
So you tell it like, you know, go to the red house
and then it's supposed to go to the red house.
But then one of the things that Jacob did
is he treated that sentence not as a command from a person,
but as a representation of the internal kind of state
of the mind of this policy, essentially.
So that when it was faced with a new task,
what it would do is it would basically try to think
of possible language descriptions,
attempt to do them and see if they led to the right outcome.
So it would kind of think out loud, like, you know,
I'm faced with this new task, what am I gonna do?
Let me go to the red house.
Oh, that didn't work.
Let me go to the blue room or something.
Let me go to the green plant.
And once it got some reward, it would say,
oh, go to the green plant, that's what's working.
I'm gonna go to the green plant.
And then you could look at the string that it came up with,
and that was a description of how it thought
it should solve the problem.
So you could basically incorporate language
as internal state and you can start getting some handle
on these kinds of things.
- And then what I was kind of trying to get to is that
also if you add to the reward function,
the convincingness of that story.
So I have another reward signal of like,
people who review that story, how much they like it.
So that, you know, initially that could be a hyper parameter
sort of hard coded heuristic type of thing,
but it's an interesting notion of the convincingness
of the story becoming part of the reward function,
the objective function of the explainability.
It's in the world of sort of Twitter and fake news,
that might be a scary notion that the nature of truth
may not be as important as the convincingness of the,
how convinced you are in telling the story around the facts.
Well, let me ask the basic question.
You're one of the world-class researchers
in reinforcement learning, deep reinforcement learning,
certainly in the robotics space.
What is reinforcement learning?
- I think that what reinforcement learning refers to today
is really just the kind of the modern incarnation
of learning-based control.
So classically reinforcement learning
has a much more narrow definition,
which is that it's literally learning from reinforcement,
like the thing does something
and then it gets a reward or punishment.
But really I think the way the term is used today
is it's used to refer more broadly to learning-based control.
So some kind of system
that's supposed to be controlling something
and it uses data to get better.
- And what does control mean?
So is action is the fundamental element there?
- It means making rational decisions.
Now, and rational decisions are decisions
that maximize a measure of utility.
- And sequentially, so you made decisions
time and time and time again.
Now, like, it's easier to see that kind of idea
in the space of maybe games, in the space of robotics.
Do you see it bigger than that?
Is it applicable?
Like, where are the limits of the applicability
of reinforcement learning?
- Yeah, so rational decision-making
is essentially the encapsulation of the AI problem
viewed through a particular lens.
So any problem that we would want a machine to do,
an intelligent machine,
can likely be represented as a decision-making problem.
Classifying images is a decision-making problem,
although not a sequential one typically.
Controlling a chemical plant is a decision-making problem.
Deciding what videos to recommend on YouTube
is a decision-making problem.
And one of the really appealing things
about reinforcement learning is,
if it does encapsulate the range
of all of these decision-making problems,
perhaps working on reinforcement learning
is one of the ways to reach a very broad swath
of AI problems.
- But what is the fundamental difference
between reinforcement learning
and maybe supervised machine learning?
- So reinforcement learning can be viewed
as a generalization of supervised machine learning.
You can certainly cast supervised learning
as a reinforcement learning problem.
You can just say your loss function
is the negative of your reward,
but you have stronger assumptions.
You have the assumption that someone actually told you
what the correct answer was,
that your data was IID and so on.
So you could view reinforcement learning
as essentially relaxing some of those assumptions.
Now that's not always a very productive way to look at it,
because if you actually have a supervised learning problem,
you'll probably solve it much more effectively
by using supervised learning methods, because it's easier.
But you can view reinforcement learning
as a generalization of that.
- No, for sure.
But they're fundamentally different.
That's a mathematical statement
that's absolutely correct.
But it seems that reinforcement learning,
the kind of tools we bring to the table today,
of today, so maybe down the line,
everything will be a reinforcement learning problem,
just like you said, image classification should be mapped
to a reinforcement learning problem.
But today, the tools and ideas,
the way we think about them are different.
Sort of supervised learning has been used very effectively
to solve basic, narrow AI problems.
Reinforcement learning kind of represents the dream of AI.
It's very much so in the research space now,
in sort of captivating the imagination of people,
of what we can do with intelligent systems,
but it hasn't yet had as wide of an impact
as the supervised learning approaches.
So my question comes from the more practical sense.
Like, what do you see is the gap
between the more general reinforcement learning
and the very specific, yes, it's sequential decision-making
with one step in the sequence of the supervised learning?
- So from a practical standpoint,
I think that one thing that is potentially
a little tough now, and this is, I think,
something that we'll see, this is a gap
that we might see closing over the next couple of years,
is the ability of reinforcement learning algorithms
to effectively utilize large amounts of prior data.
So one of the reasons why it's a bit difficult today
to use reinforcement learning for all the things
that we might wanna use it for is that
in most of the settings where we wanna do
rational decision-making, it's a little bit tough
to just deploy some policy that does crazy stuff
and learns purely through trial and error.
It's much easier to collect a lot of data,
a lot of logs of some other policy that you've got,
and then maybe if you can get a good policy out of that,
then you deploy it and let it kind of fine tune
a little bit.
§07Transcript · 49:30–57:05
But algorithmically, it's quite difficult to do that.
So I think that once we figure out how to get
reinforcement learning to bootstrap effectively
from large data sets, then we'll see
a very, very rapid growth in applications
of these technologies.
So this is what's referred to as
off-policy reinforcement learning,
or offline RL, or batch RL.
And I think we're seeing a lot of research right now
that's bringing us closer and closer to that.
- Can you maybe paint the picture of the different methods?
So you said off-policy, what's value-based
reinforcement learning?
What's policy-based?
What's model-based?
What's off-policy, on-policy?
What are the different categories of reinforcement learning?
- So one way we can think about reinforcement learning
is that it's, in some very fundamental way,
it's about learning models that can answer
kind of what-if questions.
So what would happen if I take this action
that I hadn't taken before?
And you do that, of course, from experience, from data.
And oftentimes, you do it in a loop.
So you build a model that answers these what-if questions,
use it to figure out the best action you can take,
and then go and try taking that
and see if the outcome agrees with what you predicted.
So the different kinds of techniques
basically refer to different ways of doing it.
So model-based methods answer a question
of what state you would get,
basically what would happen to the world
if you were to take a certain action.
Value-based methods, they answer the question
of what value you would get,
meaning what utility you would get.
But in a sense, they're not really all that different
because they're both really just answering
these what-if questions.
Now, unfortunately for us,
with current machine learning methods,
answering what-if questions can be really hard
because they are really questions
about things that didn't happen.
If you wanted to answer what-if questions
about things that did happen,
you wouldn't need a learned model.
You would just repeat the thing that worked before.
And that's really a big part of why RL is a little bit tough.
So if you have a purely on-policy online process,
then you ask these what-if questions,
you make some mistakes,
then you go and try doing those mistaken things,
and then you observe the counter examples
that'll teach you not to do those things again.
If you have a bunch of off-policy data
and you just want to synthesize the best policy you can
out of that data,
then you really have to deal with the challenges
of making these counterfactual.
- First of all, what's a policy?
- Yeah, a policy is a model or some kind of function
that maps from observations of the world to actions.
So in reinforcement learning,
we often refer to the current configuration
of the world as the state.
So we say the state kind of encompasses everything
you need to fully define where the world is at at the moment.
And depending on how we formulate the problem,
we might say you either get to see the state
or you get to see an observation,
which is some snapshot, some piece of the state.
- So policy just includes everything in it
in order to be able to act in this world.
- Yes.
- And so what does off-policy mean?
- So yeah, so the terms on-policy and off-policy
refer to how you get your data.
So if you get your data from somebody else
who was doing some other stuff,
maybe you get your data from some manually programmed system
that was just running in the world before,
that's referred to as off-policy data.
But if you got the data by actually acting in the world
based on what your current policy thinks is good,
we call that on-policy data.
And obviously on-policy data is more useful to you
because if your current policy makes some bad decisions,
you will actually see that those decisions are bad.
Off-policy data, however, might be much easier to obtain
because maybe that's all the log data
that you have from before.
- So we talk about, offline talked about autonomous vehicles
so you can envision off-policy kind of approaches
in robotic spaces where there's already
ton of robots out there, but they don't get the luxury
of being able to explore based on
reinforcement learning framework.
So how do we make, again, open question,
but how do we make off-policy methods work?
- Yeah, so this is something that has been
kind of a big open problem for a while.
And in the last few years,
people have made a little bit of progress on that.
You know, I can tell you about,
and it's not by any means solved yet,
but I can tell you some of the things that, for example,
we've done to try to address some of the challenges.
It turns out that one really big challenge
with off-policy reinforcement learning
is that you can't really trust your models
to give accurate predictions for any possible action.
So if I've never tried to, if in my data set,
I never saw somebody steering the car
off the road onto the sidewalk,
my value function or my model
is probably not going to predict the right thing
if I ask what would happen if I were to steer the car
off the road onto the sidewalk.
So one of the important things you have to do
to get off-policy RL to work
is you have to be able to figure out
whether a given action will result
in a trustworthy prediction or not.
And you can use kind of distribution estimation methods,
kind of density estimation methods
to try to figure that out.
So you could figure out that, well, this action,
my model is telling me that it's great,
but it looks totally different
from any action I've taken before,
so my model is probably not correct.
And you can incorporate regularization terms
into your learning objective
that will essentially tell you not to ask those questions
that your model is unable to answer.
- What would lead to breakthroughs in this space,
do you think?
Like what's needed?
Is this a data set question?
Do we need to collect big benchmark data sets
that allow us to explore the space?
Is it a new kinds of methodologies?
Like what's your sense?
Or maybe coming together in a space of robotics
and defining the right problem to be working on.
- I think for off-policy reinforcement learning
in particular, it's very much
an algorithms question right now.
And this is something that I think is great
because an algorithms question is,
that that just takes some very smart people to get together
and think about it really hard.
Whereas if it was like a data problem or hardware problem,
that would take some serious engineering.
So that's why I'm pretty excited about that problem
because I think that we're in a position
where we can make some real progress on it
just by coming up with the right algorithms.
In terms of which algorithms they could be,
the problems at their core are very related to problems
in things like causal inference, right?
Because what you're really dealing with is situations
where you have a model, a statistical model,
that's trying to make predictions
about things that it hadn't seen before.
And if it's a model that's generalizing properly,
that'll make good predictions.
If it's a model that picks up on spurious correlations
that will not generalize properly.
And then you have an arsenal of tools you could use.
You could, for example, figure out
what are the regions where it's trustworthy,
or on the other hand,
you could try to make it generalize better somehow,
or some combination of the two.
- Is there room for mixing, sort of,
where most of it, like 90, 95% is off policy,
you already have the dataset,
and then you get to send the robot out
to do a little exploration?
Like, what's that role of mixing them together?
- Yeah, absolutely.
I think that this is something that you actually
described very well at the beginning of our discussion
when you talked about the iceberg.
Like, this is the iceberg.
The 99% of your prior experience,
that's your iceberg.
You'd use that for off-policy reinforcement learning.
And then, of course, if you've never, you know,
opened that particular kind of door
with that particular lock before,
then you have to go out and fiddle with it a little bit,
and that's that additional 1%
to help you figure out a new task.
And I think that's actually, like,
§08Transcript · 57:06–1:05:41
a pretty good recipe going forward.
- Is this, to you, the most exciting space
of reinforcement learning now?
Or is there, what's, and maybe taking a step back,
not just now, but what's, to you,
is the most beautiful idea?
Apologize for the romanticized question,
but the beautiful idea or concept
in reinforcement learning?
- In general, I actually think that one of the things
that is a very beautiful idea in reinforcement learning
is just the idea that you can obtain a near-optimal control
or a near-optimal policy without actually having
a complete model of the world.
This is, you know, it's something that feels
perhaps kind of obvious if you just hear
the term reinforcement learning
or you think about trial and error learning,
but from a control's perspective, it's a very weird thing
because classically, you know, we think about
engineered systems and controlling engineered systems
as the problem of writing down some equations
and then figuring out, given these equations,
you know, basically like solve for X,
figure out the thing that maximizes its performance.
And the theory of reinforcement learning
actually gives us a mathematically principled framework
to reason about, you know, optimizing some quantity
when you don't actually know the equations
that govern that system.
And that, I don't know, to me, that actually seems
kind of, you know, very elegant,
not something that sort of becomes immediately obvious,
at least in the mathematical sense.
- Does it make sense to you that it works at all?
- Well, I think it makes sense when you take some time
to think about it, but it is a little surprising.
- Well, then taking a step into the more
deeper representations, which is also very surprising,
of sort of the richness of the state space,
the space of environments that this kind of approach
can operate in, can you maybe say
what is deep reinforcement learning?
- Well, deep reinforcement learning simply refers
to taking reinforcement learning algorithms
and combining them with high capacity
neural net representations, which is, you know,
kind of, it might at first seem like a pretty arbitrary
thing, just take these two components
and stick them together.
But the reason that it's something that has become
so important in recent years is that reinforcement learning,
it kind of faces an exacerbated version of a problem
that has faced many other machine learning techniques.
So if we go back to like, you know, the early 2000s
or the late 90s, we'll see a lot of research
on machine learning methods that have some very appealing
mathematical properties, like they reduce
the convex optimization problems, for instance,
but they require very special inputs.
They require a representation of the input
that is clean in some way, like for example,
clean in the sense that the classes
in your multi-class classification problems
separate linearly.
So they have some kind of good representation
and we call this a feature representation.
And for a long time, people were very worried
about features in the world of supervised learning
because somebody had to actually build those features.
So you couldn't just take an image and plug it
into your logistic regression or your SVM or something.
Someone had to take that image and process it
using some handwritten code.
And then neural nets came along
and they could actually learn the features.
And suddenly we could apply learning directly
to the raw inputs, which was great for images,
but it was even more great for all the other fields
where people hadn't come up with good features yet.
And one of those fields is actually reinforcement learning
because in reinforcement learning,
the notion of features, if you don't use neural nets
and you have to design your own features,
is very, very opaque.
Like it's very hard to imagine,
let's say I'm playing chess or go,
what is a feature with which I can represent
the value function for go
or even the optimal policy for go linearly?
Like, I don't even know how to start thinking about it.
And people tried all sorts of things.
They would write down, you know,
an expert chess player looks for whether the knight
is in the middle of the board or not.
So that's a feature, is knight in middle of board?
And they would write these like long lists
of kind of arbitrary made up stuff.
And that was really kind of getting us nowhere.
- And that's a little, chess is a little more accessible
than the robotics problem.
- Absolutely.
- Right, there's at least experts
in the different features for chess.
But still like the neural network there,
to me that's, I mean, you put it eloquently
and almost made it seem like a natural step
to add neural networks.
But the fact that neural networks are able
to discover features in the control problem,
it's very interesting, it's hopeful.
I'm not sure what to think about it,
but it feels hopeful that the control problem
has features to be learned.
- Like, I guess my question is,
is it surprising to you how far the deep side
of deep reinforcement learning was able to,
like what the space of problems has been able to tackle
from, especially in games with the AlphaStar
and AlphaZero and just the representation power there
and in the robotics space.
And what is your sense of the limits
of this representation power and the control context?
- I think that in regard to the limits that here,
I think that one thing that makes it a little hard
to fully answer this question is because in settings
where we would like to push these things to the limit,
we encounter other bottlenecks.
So like the reason that I can't get my robot
to learn how to like, I don't know,
do the dishes in the kitchen,
it's not because its neural net is not big enough.
It's because when you try to actually do trial
and error learning, reinforcement learning directly
in the real world, where you have the potential
to gather these large, highly varied and complex datasets,
you start running into other problems.
Like one problem you run into very quickly,
it'll first sound like a very pragmatic problem,
but it actually turns out to be a pretty deep scientific
problem, take the robot, put it in your kitchen,
have it try to learn to do the dishes with trial and error,
it'll break all your dishes
and then we'll have no more dishes to clean.
Now you might think this is a very practical issue,
but there's something to this,
which is that if you have a person trying to do this,
a person will have some degree of common sense,
they'll break one dish,
they'll be a little more careful with the next one.
And if they break all of them,
they're gonna go and get more or something like that.
So there's all sorts of scaffolding
that comes very naturally to us for our learning process,
like if I have to learn something through trial and error,
I have the common sense to know that I have to try multiple
times, if I screw something up, I ask for help,
or I reset things or something like that.
And all of that is kind of outside of the classic
reinforcement learning problem formulation.
There are other things that can also be categorized
as kind of scaffolding, but are very important.
Like for example, where do you get your reward function?
If I wanna learn how to pour a cup of water,
well, how do I know if I've done it correctly?
Now that probably requires an entire computer vision system
to be built just to determine that.
And that seems a little bit inelegant.
So there are all sorts of things like this
that start to come up when we think through
what we really need to get reinforcement learning
to happen at scale in the real world.
And I think that many of these things actually suggest
a little bit of a shortcoming in the problem formulation
and a few deeper questions that we have to resolve.
- That's really interesting.
I talked to like David Silver about AlphaZero,
and it seems like there's no, again,
that we haven't hit the limit at all
in the context when there's no broken dishes.
So in the case of Go, you can,
it's really about just scaling compute.
So again, like the bottleneck is the amount of money
you're willing to invest in compute,
and then maybe the different, the scaffolding around
how difficult it is to scale compute maybe.
But there, there's no limit.
And it's interesting.
Now we move to the real world and there's the broken dishes,
there's all the, and the reward function like you mentioned.
That's really nice.
So what, how do we push forward there?
Do you think, there's this kind of sample efficiency
question that people bring up of, you know,
not having to break 100,000 dishes.
Is this an algorithm question?
Is this a data selection like question?
What do you think?
How do we, how do we not break too many dishes?
- Yeah, well, one way we can think about that is that
§09Transcript · 1:05:44–1:14:43
maybe we need to be better at reusing our data,
building that iceberg.
So perhaps it's too much to hope that
you can have a machine that's in isolation,
in the vacuum without anything else,
can just master complex tasks in like in minutes,
the way that people do.
But perhaps it also doesn't have to.
Perhaps what it really needs to do is have an existence,
a lifetime where it does many things
and the previous things that it has done,
prepare it to do new things more efficiently.
And, you know, the study of these kinds of questions
typically falls under categories like multitask learning
or meta learning, but they all fundamentally deal
with the same general theme, which is use experience
for doing other things to learn to do new things
efficiently and quickly.
- So what do you think about,
if you just look at one particular case study
of a Tesla autopilot that has quickly approaching
towards a million vehicles on the road,
where some percentage of the time, 30, 40% of the time
is driven using the computer vision,
multitask, hydranet, right?
And then the other percent,
that's what they call it, hydranet.
The other percent is human controlled.
From the human side, how can we use that data?
What's your sense?
So like, what's the signal?
Do you have ideas in this autonomous vehicle space
when people can lose their lives?
You know, it's a safety critical environment.
So how do we use that data?
- So I think that actually the kind of problems
that come up when we want systems that are reliable
and that can kind of understand the limits
of their capabilities,
they're actually very similar to the kind of problems
that come up when we're doing
off-policy reinforcement learning.
So as I mentioned before,
in off-policy reinforcement learning,
the big problem is you need to know
when you can trust the predictions of your model,
because if you're trying to evaluate
some pattern of behavior for which your model
doesn't give you an accurate prediction,
then you shouldn't use that to modify your policy.
And it's actually very similar to the problem
that we're faced when we actually then deploy that thing
and we want to decide whether we trust it
in the moment or not.
So perhaps we just need to do a better job
of figuring out that part.
And that's a very deep research question, of course,
but it's also a question that a lot of people are working on.
So I'm pretty optimistic that we can make some progress
on that over the next few years.
- What's the role of simulation in reinforcement learning,
deep reinforcement learning, reinforcement learning?
Like how essential is it?
It's been essential for the breakthroughs so far,
for some interesting breakthroughs.
Do you think it's a crutch that we rely on?
I mean, again, this connects to our off policy discussion,
but do you think we can ever get rid of simulation
or do you think simulation will actually take over?
We'll create more and more realistic simulations
that will allow us to solve actual real world problems,
like transfer the models we learn in simulation
to real world problems.
- Yeah.
I think that simulation is a very pragmatic tool
that we can use to get a lot of useful stuff
to work right now.
But I think that in the long run,
we will need to build machines that can learn
from real data, because that's the only way
that we'll get them to improve perpetually.
Because if we can't have our machines learn from real data,
if they have to rely on simulated data,
eventually the simulator becomes the bottleneck.
In fact, this is a general thing.
If your machine has any bottleneck that is built by humans
and that doesn't improve from data,
it will eventually be the thing that holds it back.
And if you're entirely reliant on your simulator,
that'll be the bottleneck.
If you're entirely reliant on a manually designed controller,
that's gonna be the bottleneck.
So simulation is very useful.
It's very pragmatic, but it's not a substitute
for being able to utilize real experience.
And this is, by the way, this is something
that I think is quite relevant now,
especially in the context of some of the things
we've discussed, because some of these kind
of scaffolding issues that I mentioned,
things like the broken dishes
and the unknown reward function,
like these are not problems that you would ever stumble on
when working in a purely simulated kind of environment.
But they become very apparent
when we try to actually run these things in the real world.
- To throw a brief wrench into our discussion, let me ask,
do you think we're living in a simulation?
- Oh, I have no idea.
- Do you think that's a useful thing to even think about,
about the fundamental physics nature of reality?
Or another perspective, the reason I think
the simulation hypothesis is interesting
is to think about how difficult is it to create
sort of a virtual reality game type situation
that will be sufficiently convincing to us humans,
or sufficiently enjoyable that we wouldn't wanna leave.
I mean, that's actually a practical engineering challenge.
And I personally really enjoy virtual reality,
but it's quite far away, but I kind of think about
what would it take for me to wanna spend more time
in virtual reality versus the real world?
And that's a sort of a nice, clean question,
because at that point, we've reached,
if I wanna live in a virtual reality,
that means we're just a few years away,
we're a majority of the population lives in a virtual reality
and that's how we create the simulation, right?
You don't need to actually simulate the quantum gravity
and just every aspect of the universe.
And that's an interesting question
for reinforcement learning, too,
is if we wanna make sufficiently realistic simulations
that may, it blend the difference between
sort of the real world and the simulation,
thereby just some of the things we've been talking about,
kind of the problems go away,
if we can create actually interesting, rich simulations.
- It's an interesting question.
And it actually, I think your question
casts your previous question in a very interesting light,
because in some ways, asking whether we can,
well, the more kind of practical version of this,
like, can we build simulators that are good enough
to train essentially AI systems that will work in the world?
And it's kind of interesting to think about this,
about what this implies, if true,
it kind of implies that it's easier to create the universe
than it is to create a brain.
And that seems like, put this way, it seems kind of weird.
- The aspect of the simulation most interesting to me
is the simulation of other humans.
That seems to be a complexity
that makes the robotics problem harder.
Now, I don't know if every robotics person
agrees with that notion, just as a quick aside,
what are your thoughts about when the human enters
the picture of the robotics problem?
How does that change the reinforcement learning problem,
the learning problem in general?
- Yeah, I think that's a, it's a kind of a complex question.
And I guess my hope for a while had been that
if we build these robotic learning systems
that are multitask, that utilize lots of prior data
and that learn from their own experience,
the bit where they have to interact with people
will be perhaps handled in much the same way
as all the other bits.
So if they have prior experience of interacting with people
and they can learn from their own experience
of interacting with people for this new task,
maybe that'll be enough.
Now, of course, if it's not enough,
there are many other things we can do.
And there's quite a bit of research in that area.
But I think it's worth a shot to see
whether the multi-agent interaction,
the ability to understand that other beings in the world
have their own goals, intentions, and thoughts, and so on,
whether that kind of understanding can emerge automatically
from simply learning to do things with and maximize utility.
- That information arises from the data.
You've said something about gravity,
that you don't need to explicitly inject anything
into the system that can be learned from the data.
And gravity is an example of something
that could be learned from data,
sort of like the physics of the world.
What are the limits of what we can learn from data?
So a very simple, clean way to ask that is,
do you really think we can learn gravity from just data?
The idea, the laws of gravity.
- So something that I think is a common kind of pitfall
when thinking about prior knowledge and learning
is to assume that just because we know something,
then that it's better to tell the machine about that
rather than have it figure it out on its own.
In many cases, things that are important
that affect many of the events
that the machine will experience
§10Transcript · 1:14:45–1:23:16
are actually pretty easy to learn.
Like, if things, if every time you drop something,
it falls down, like, yeah, you might not get the,
you might get kind of the Newton's version,
not Einstein's version, but it'll be pretty good.
And it will probably be sufficient for you
to act rationally in the world
because you see the phenomenon all the time.
So things that are readily apparent from the data,
we might not need to specify those by hand.
It might actually be easier
to let the machine figure them out.
- It just feels like that there might be a space
of many local minima in terms of theories of this world
that we would discover and get stuck on.
- Yeah, of course.
- That Newtonian mechanics is not necessarily
easy to come by.
- Yeah, and well, in fact, in some fields of science,
for example, human civilizations
that sell full of these local optimums.
So for example, if you think about how people
tried to figure out biology and medicine,
for the longest time, the kind of rules,
the kind of principles that serve us very well
in our day-to-day lives actually serve us very poorly
in understanding medicine and biology.
We had kind of very superstitious and weird ideas
about how the body worked until the advent
of the modern scientific method.
So that does seem to be a failing of this approach,
but it's also a failing of human intelligence, arguably.
- Maybe a small aside, but some,
the idea of self-play is fascinating
in reinforcement learning, sort of these competitive,
creating a competitive context in which agents
can play against each other in a,
sort of at the same skill level
and thereby increasing each other's skill level.
It seems to be this kind of self-improving mechanism
is exceptionally powerful in the context
where it could be applied.
First of all, is that beautiful to you
that this mechanism work as well as it does
and also can be generalized to other contexts
like in the robotic space or anything
that's applicable to the real world?
- I think that it's a very interesting idea,
but I suspect that the bottleneck
to actually generalizing it to the robotic setting
is actually going to be the same
as the bottleneck for everything else,
that we need to be able to build machines
that can get better and better
through natural interaction with the world.
And once we can do that,
then they can go out and play with,
they can play with each other, they can play with people,
they can play with the natural environment.
But before we get there,
we've got all these other problems
we've got, we have to get out of the way.
- So there's no shortcut around that.
You have to interact with a natural environment that.
- Well, because in a self-play setting,
you still need a mediating mechanism.
So the reason that self-play works for a board game
is because the rules of that board game
mediate the interaction between the agents.
So the kind of intelligent behavior that will emerge
depends very heavily on the nature
of that mediating mechanism.
- So on the side of reward functions,
that's coming up with good reward functions
seems to be the thing that we associate
with general intelligence,
like human beings seem to value the idea
of developing our own reward functions
of arriving at meaning and so on.
And yet for reinforcement learning,
we often kind of specify this, the given.
What's your sense of how we develop
good reward functions?
- Yeah, I think that's a very complicated
and very deep question.
And you're completely right that classically
in reinforcement learning,
this question has kind of been treated as a non-issue
that you sort of treat the reward as this external thing
that comes from some other bit of your biology
and you kind of don't worry about it.
And I do think that that's actually,
a little bit of a mistake that we should worry about it.
And we can approach it in a few different ways.
We can approach it, for instance,
by thinking of reward as a communication medium.
We can say, well, how does a person communicate
to a robot what its objective is?
You can approach it also as a sort of more
of an intrinsic motivation medium.
You could say, can we write down
kind of a general objective that leads to good capability?
Like, for example, can you write down some objectives
such that even in the absence of any other task,
if you maximize that objective,
you'll sort of learn useful things.
This is something that has sometimes been called
unsupervised reinforcement learning,
which I think is a really fascinating area of research,
especially today.
We've done a bit of work on that recently.
One of the things we've studied is whether
we can have some notion of unsupervised reinforcement
learning by means of information theoretic quantities,
like for instance, minimizing a Bayesian measure of surprise.
This is an idea that was pioneered actually
in the computational neuroscience community
by folks like Carl Friston.
And we've done some work recently that shows
that you can actually learn pretty interesting skills
by essentially behaving in a way that allows you
to make accurate predictions about the world.
It seems a little circular, like do the things
that will lead to you getting the right answer
for prediction.
But you can, by doing this, you can sort of discover
stable niches in the world.
You can discover that if you're playing Tetris,
then correctly clearing the rows will let you play Tetris
for longer and keep the board nice and clean,
which sort of satisfies some desire for order in the world.
And as a result, get some degree of leverage
over your domain.
So we're exploring that pretty actively.
- Is there a role for a human notion of curiosity
in itself being the reward, sort of discovering new things
about the world?
- So one of the things that I'm pretty interested in
is actually whether discovering new things
can actually be an emergent property
of some other objective that quantifies capability.
So new things for the sake of new things,
maybe it might not by itself be the right answer,
but perhaps we can figure out an objective
for which discovering new things
is actually the natural consequence.
That's something we're working on right now,
but I don't have a clear answer for you there yet.
That's still a work in progress.
- You mean just as a curious observation
to see sort of creative patterns of curiosity
on the way to optimize for a particular--
- On the way to optimize
for a particular measure of capability.
- Is there ways to understand or anticipate unexpected,
unintended consequences of particular reward functions,
sort of anticipate the kind of strategies
that might be developed
and try to avoid highly detrimental strategies?
- Yeah, so classically, this is something
that has been pretty hard in reinforcement learning
because it's difficult for a designer
to have good intuition about
what a learning algorithm will come up with
when they give it some objective.
There are ways to mitigate that.
One way to mitigate it is to actually define an objective
that says like, don't do weird stuff.
You can actually quantify it.
You can say just like, don't enter situations
that have low probability under the distribution of states
you've seen before.
It turns out that that's actually one very good way
to do off-policy reinforcement learning actually.
So we can do some things like that.
- If we slowly venture in speaking about reward functions
into greater and greater levels of intelligence,
there's, I mean, Stuart Russell thinks about this,
the alignment of AI systems with us humans.
So how do we ensure that AGI systems align with us humans?
It's kind of a reward function question
of specifying the behavior of AI systems
such that their success aligns
with the broader intended success interest of human beings.
Do you have thoughts on this?
Do you have kind of concerns
of where reinforcement learning fits into this?
Or are you really focused on the current moment
of us being quite far away
and trying to solve the robotics problem?
- I don't have a great answer to this,
but, you know, and I do think that this is a problem
that's important to figure out.
For my part, I'm actually a bit more concerned
about the other side of this equation that, you know,
maybe rather than unintended consequences
for objectives that are specified too well,
I'm actually more worried right now
about unintended consequences for objectives
that are not optimized well enough,
§11Transcript · 1:23:18–1:32:21
which might become a very pressing problem
when we, for instance, try to use these techniques
for safety critical systems like cars and aircraft and so on.
I think at some point we'll face the issue
of objectives being optimized too well,
but right now I think we're more likely
to face the issue of them not being optimized well enough.
- But you don't think unintended consequences can arise
even when you're far from optimality,
sort of like on the path to it?
- Oh no, I think unintended consequences
can absolutely arise.
It's just, I think right now the bottleneck
for improving reliability, safety, and things like that
is more with systems that like need to work better,
that need to optimize their objective better.
- Do you have thoughts, concerns about existential threats
of human level intelligence?
Sort of, if we put on our hat of looking in 10, 20, 100,
500 years from now, do you have concerns
about existential threats of AI systems?
- I think there are absolutely existential threats
for AI systems, just like there are
for any powerful technology.
But I think that these kinds of problems
can take many forms and some of those forms
will come down to people with nefarious intent.
Some of them will come down to AI systems
that have some fatal flaws.
And some of them will, of course, come down to AI systems
that are too capable in some way.
But among this set of potential concerns,
I would actually be much more concerned
about the first two right now,
and principally the one with nefarious humans,
because just through all of human history,
actually it's the nefarious humans that have been
the problem, not the nefarious machines,
than I am about the others.
And I think that right now the best that I can do
to make sure things go well is to build
the best technology I can and also hopefully
to promote responsible use of that technology.
- Do you think RL systems has something to teach us, humans?
You said nefarious humans getting us in trouble.
I mean, machine learning systems have in some ways
have revealed to us the ethical flaws in our data.
In that same kind of way, can reinforcement learning
teach us about ourselves?
Has it taught something?
What have you learned about yourself
from trying to build robots
and reinforcement learning systems?
- I'm not sure what I've learned about myself,
but maybe part of the answer to your question
might become a little bit more apparent
once we see more widespread deployment
of reinforcement learning for decision-making support
in domains like healthcare, education,
social media, et cetera.
And I think we will see some interesting stuff emerge there.
We will see, for instance, what kind of behaviors
these systems come up with in situations
where there is interaction with humans
and where they have possibility
of influencing human behavior.
I think we're not quite there yet,
but maybe in the next few years,
we'll see some interesting stuff come out in that area.
- I hope outside the research space,
'cause the exciting space where this could be observed
is sort of large companies that deal with large data.
And I hope there's some transparency.
And one of the things that's unclear
when I look at social networks and just online
is why an algorithm did something
or whether even an algorithm was involved.
And that'd be interesting from a research perspective
just to observe the results of algorithms
to open up that data
or to at least be sufficiently transparent
about the behavior of these AI systems in the real world.
What's your sense?
I don't know if you looked at the blog post,
Bitter Lesson by Rich Sutton,
where it looks at sort of the big lesson
of researching AI and reinforcement learning
is that simple methods, general methods
that leverage computation seem to work well.
So basically don't try to do any kind of fancy algorithms,
just wait for computation to get fast.
Do you share this kind of intuition?
I think the high level idea makes a lot of sense.
I'm not sure that my takeaway would be
that we don't need to work on algorithms.
I think that my takeaway would be
that we should work on general algorithms.
And actually I think that this idea
of needing to better automate
the acquisition of experience in the real world
actually follows pretty naturally
from Rich Sutton's conclusion.
So if the claim is that automated general methods
plus data leads to good results,
then it makes sense that we should build general methods
and we should build the kind of methods
that we can deploy and get them to go out there
and like collect their experience autonomously.
I think that one place where I think
that the current state of things
falls a little bit short of that
is actually the going out there
and collecting the data autonomously,
which is easy to do in a simulated board game,
but very hard to do in the real world.
- Yeah, it keeps coming back to this one problem, right?
So your mind is focused there now in this real world.
It just seems scary, this step of collecting the data.
And it seems unclear to me how we can do it effectively.
- Yeah, well, you know, seven billion people in the world,
each of them have to do that at some point in their lives.
- And we should leverage that experience
that they've all done.
We should be able to try to collect that kind of data.
Okay, big questions.
Maybe stepping back through your life,
would book or books, technical or fiction or philosophical,
had a big impact on the way you saw the world,
on the way you thought about in the world,
your life in general?
And maybe what books, if it's different,
would you recommend people consider reading on their own?
- Intellectual journey.
It could be within reinforcement learning,
but it could be very much bigger.
- I don't know if this is like a scientifically,
like particularly meaningful answer,
but like the honest answer is that I actually found
a lot of the work by Isaac Asimov
to be very inspiring when I was younger.
I don't know if that has anything to do
with AI necessarily.
- You don't think it had a ripple effect in your life?
- Maybe it did.
But yeah, I think that a vision of a future where,
well, first of all, artificial,
I might say artificial intelligence system,
artificial robotic systems have kind of a big place,
a big role in society.
And where we try to imagine the sort of the limiting case
of technological advancement and how that might play out
in our future history.
But yeah, I think that that was in some way influential.
I don't really know how, but I would recommend it.
I mean, if nothing else, you'd be well entertained.
- When did you first yourself like fall in love
with the idea of artificial intelligence,
get captivated by this field?
- So my honest answer here is actually that
I only really started to think about it
as something that I might want to do
actually in graduate school pretty late.
And a big part of that was that until,
somewhere around 2009, 2010,
it just wasn't really high on my priority list
because I didn't think that it was something
where we're going to see very substantial advances
in my lifetime.
And maybe in terms of my career,
the time when I really decided I wanted to work on this
was when I actually took a seminar course
that was taught by Professor Andrew Ng.
And at that point, I of course had some,
he had like a decent understanding
of the technical things involved.
But one of the things that really resonated with me
was when he said in the opening lecture,
something to the effect of like,
well, he used to have graduate students come to him
and talk about how they want to work on AI
and he would kind of chuckle
and give them some math problem to deal with.
But now he's actually thinking that this is an area
where we might see like substantial advances
in our lifetime.
And that kind of got me thinking because,
you know, in some abstract sense,
yeah, like you can kind of imagine that,
but in a very real sense,
when someone who had been working on that kind of stuff
their whole career suddenly says that,
yeah, like that had some effect on me.
- Yeah, this might be a special moment
in the history of the field.
That this is where we might see
some interesting breakthroughs.
So in the space of advice,
somebody who's interested in getting started
in machine learning or reinforcement learning,
what advice would you give to maybe an undergraduate student
§12Transcript · 1:32:23–1:37:24
or maybe even younger,
how, what are the first steps to take
and further on, what are the steps to take on that journey?
- So something that I think is important to do
is to not be afraid to like spend time
imagining the kind of outcome that you might like to see.
So, you know, one outcome might be a successful career,
a large paycheck or something,
or state-of-the-art results on some benchmark,
but hopefully that's not the thing
that's like the main driving force for somebody.
But I think that if someone who's a student
considering a career in AI,
like takes a little while, sits down and thinks like,
what do I really want to see?
What I want to see a machine do?
What do I want to see a robot do?
What do I want to do in,
what I want to see a natural language system?
Just like imagine, you know,
imagine it almost like a commercial
for a future product or something,
or like something that you'd like to see in the world,
and then actually sit down and think about the steps
that are necessary to get there.
And hopefully that thing is not a better number
on ImageNet classification.
It's like, it's probably like an actual thing
that we can't do today that would be really awesome.
Whether it's a robot butler or a, you know,
a really awesome healthcare decision-making support system,
whatever it is that you find inspiring.
And I think that thinking about that
and then backtracking from there
and imagining the steps needed to get there
will actually lead to much better research.
It'll lead to rethinking the assumptions.
It'll lead to working on the bottlenecks
that other people aren't working on.
- And then naturally to turn to you,
we've talked about reward functions,
and you just give an advice on looking forward
how you'd like to see,
what kind of change you would like to make in the world.
What do you think, ridiculous, big question,
what do you think is the meaning of life?
What is the meaning of your life?
What gives you fulfillment, purpose, happiness, and meaning?
- That's a very big question.
- What's the reward function under which you're operating?
- Yeah, I think one thing that does give, you know,
if not meaning, at least satisfaction
is some degree of confidence
that I'm working on a problem that really matters.
I feel like it's less important to me
to like actually solve a problem,
but it's quite nice to take things to spend my time on
that I believe really matter.
And I try pretty hard to look for that.
- I don't know if it's easy to answer this,
but if you're successful, what does that look like?
What's the big dream?
Now, of course, success is built on top of success,
and you keep going forever, but what is the dream?
- Yeah, so one very concrete thing,
or maybe as concrete as it's gonna get here
is to see machines that actually get better and better
the longer they exist in the world.
And that kind of seems like on the surface,
one might even think that that's something
that we have today, but I think we really don't.
I think that there is an unending complexity
in the universe, and to date,
all of the machines that we've been able to build
don't sort of improve up to the limit of that complexity.
They hit a wall somewhere.
Maybe they hit a wall because they're in a simulator
that is only a very limited,
very pale imitation of the real world,
or they hit a wall because they rely on a labeled dataset,
but they never hit the wall
of like running out of stuff to see.
So I'd like to build a machine
that can go as far as possible in that regard.
- Runs up against the ceiling
of the complexity of the universe.
- Yes.
- Well, I don't think there's a better way to end it, Sergey.
Thank you so much.
It's a huge honor.
I can't wait to see the amazing work
that you have to publish in the education space
in terms of reinforcement learning.
Thank you for inspiring the world.
Thank you for the great research you do.
- Thank you.
- Thanks for listening to this conversation
with Sergey Lavin, and thank you to our sponsors,
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Intelligence without ambition is a bird without wings.
Thank you for listening, and hope to see you next time.
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