Structural Return Maximization for Reinforcement Learning Josh Joseph Alborz Geramifard Javier Velez Jonathan How Nicholas Roy 1

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Structural Return Maximization for Reinforcement Learning

Josh JosephAlborz Geramifard

Javier Velez Jonathan HowNicholas Roy

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How should we act in the presence of complex, unknown dynamics?

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What do I mean by complex dynamics?

• Can’t derive from first principles / intuition• Any dynamics model will be approximate• Limited data– Otherwise just do nearest neighbors

• Batch data– Trying to keep it as simple as possible for now– Fairly straightforward to extend to active learning

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What do I mean by complex dynamics?

• Can’t derive from first principles / intuition• Any dynamics model will be approximate• Limited data• Batch data– Fairly straightforward to extend to active learning

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How does RL solve these problems?

• Assume some representation class for:– Dynamics model– Value function– Policy

• Collect some data• Find the “best” representation based on the

data

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• Assume some representation class for:– Dynamics model– Value function– Policy

• Collect some data• Find the “best” representation based on the

data

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• The “best” representation based on the data

• This defines the best policy…not the best representation

Value (return)


Policy

Starting state

reward unknown dynamics model

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Value (return)


Policy

Starting state


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Value (return)


Policy

Starting state


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…but does RL actually solve this problem?

• Policy Search– Policy directly parameterized by

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Number of episodes

Empirical estimate

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Number of episodes

Empirical estimate

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• Model-based RL– Dynamics model =

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Maximizing likelihood != maximizing return

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Maximizing likelihood != maximizing return

…similar story for value-based methods

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ML model selection in RL

• So why do we do it?– It’s easy– It sometimes works really well– Intuitively it feels like finding the most likely model

should result in a high performing policy• Why does it fail?– Chooses an “average” model based on the data– Ignores reward function

• What do we do then?

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Our Approach


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Our Approach


Empirical estimate

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Our Approach


Empirical estimate

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Planning with Misspecified Model Classes

Us

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Our Approach


Empirical estimate

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Our Approach


Empirical estimate

We can do the same thing in a value-based setting.

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…but

• We are indirectly choosing a policy representation

• The win of this indirect representation is that it can be “small”

• Small = less data?– Intuitively you’d think so– Empirical evidence from toy problems

• But all of our guarantees rely on infinite data• …maybe there’s a way to be more concrete

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…but

• We are indirectly choosing a policy representation

• The win of this indirect representation is that it can be “small”

• Small = less data?– Intuitively you’d think so– Empirical evidence from toy problems

• But all of our guarantees rely on infinite data• …maybe there’s a way to be more concrete

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What we want

• How does the representation space relate to true return?

• …they’ve been doing this in classification since the 60s– Relationship between the bound and “size” of the

representation space / amount of data

≈?

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What we want


• …they’ve been doing this in classification since the 60s– Relationship between the bound and “size” of the

representation space / amount of data

≈?

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What we want


• …they’ve been doing this in classification since the 60s– Relationship between the “size” of the

representation space and the amount of data

≈?

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How to get there

Model-based, value-based, policy search

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How to get there


Map RL to classification Empirical Risk Minimization

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How to get there



Measuring function class size Bound on true risk

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How to get there




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How to get there




Structure of function classes Structural risk minimization

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How to get there





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Classification

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Classification

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Classification

f

𝑓 ([𝑥1𝑥2])=𝑠𝑖𝑔𝑛([𝜃1𝜃2]𝑇

[𝑥1𝑥2])

𝑥1

𝑥2

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Classification

Risk

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Classification

Loss (cost)

Risk Unknown datadistribution

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Empirical Risk Minimization

Unknown datadistribution

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Number of samples

Empirical estimate

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Mapping RL to Classification

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How to get there





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Measuring the size of a function class:VC Dimension

• Introduces a notion of “shattering”– I pick the inputs– You pick the labels– VC Dim = max number of points I can perfectly

decide

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decide

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decide

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decide

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decide

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decide

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decide

𝑉𝐶𝐷𝑖𝑚()=3

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decide• Magically, shattering (VC Dim) can be used to

bound true risk

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bound true risk

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bound true risk

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For those of you familiar with statistical learning theory…

• VC Dim – Only known for a few function classes– Difficult to estimate, bound

• Rademacher complexity– Use the data to estimate the “volume” of the

function class– This volume can then be used in a similar bound

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Measuring the size of a function class

• Now we can say concrete things about why we may prefer one representation over another with limited data

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• Now we can say concrete things about why we may prefer one representation over another with limited data

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How to get there





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Number of samples

Empirical estimate

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Empirical Risk Minimization and Limited Data


But if we have limited data we cannot expect small empirical risk to result in small true risk

Empirical estimate

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• If the bound is large, we cannot expect small empirical risk to result in small true risk

• …so what do we do?• Choose the function class which minimizes the

bound!

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• If the bound is large, we cannot expect small empirical risk to result in small true risk

• …so what do we do?• Choose the function class which minimizes the

bound!

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Structural Risk Minimization

• Using a “structure” of function classes

• For N data, we choose the function class:

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Many natural structures of policy classes!

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• We choose the function class:

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Is this Bayesian?

• Prior knowledge– Structure encodes prior knowledge

• Robust to over-fitting– Choose the function class based on risk bound

• No Bayes update• No assumptions about the true function lying

in the structure– Breaks most (all?) Bayesian nonparametrics

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Is this Bayesian?



• No Bayes update• No assumptions about the true function lying

in the structure– Breaks most (all?) Bayesian nonparametrics

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Is this Bayesian?



• No Bayes update• No assumption that the true function is

somewhere in the structure– Breaks most (all?) Bayesian nonparametrics

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Contribution

• Classification to RL mapping• Transferred probabilistic bounds from

statistical learning theory to RL• Applied structural risk minimization to RL

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Contribution

• Classification to RL mapping• Transferred probabilistic bounds from

statistical learning theory to RL• Applied structural risk minimization to RL

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Backup Slides

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From last time…

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From last time…

{𝒎𝒄 ,𝒎𝒑 ,𝒍 }

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From last time…

≈?

{𝒎𝒄 ,𝒎𝒑 ,𝒍 }

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• Rademacher complexity

Documents

Structural Return Maximization for Reinforcement Learning Josh Joseph Alborz Geramifard Javier Velez Jonathan How Nicholas Roy 1