kernelized value function approximation for reinforcement learning

Kernelized Value Function Approximation for

Reinforcement Learning

Gavin Taylor and Ronald ParrDuke University

Overview

Solve for valuefunction givenkernelized model

Solve for modelas in GPRL

KernelizedModel

Kernel: k(s,s’)Training Data:(s,r,s’),(s,r,s’)(s,r,s’)… Solve for value directly using

KLSTD or GPTD

V=Kw

KernelizedValue Function

Overview - Contributions

• Construct new model-based VFA• Equate novel VFA with previous work• Decompose Bellman Error into reward and

transition error• Use decomposition to understand VFA

€

BE(Kw) = ΔR + γΔK 'wrewarderror

transitionerror

BellmanError

Samples

Model VFA

Outline

• Motivation, Notation, and Framework• Kernel-Based Models

– Model-Based VFA– Interpretation of Previous Work

• Bellman Error Decomposition• Experimental Results and Conclusions

Markov Reward Processes

• M=(S,P,R,) • Value: V(s)=expected, discounted sum of

rewards from state s• Bellman equation:

• Bellman equation in matrix notation:

€

V[si] = ri + γ P(s j | si)V[s j ]s j ∈S

∑

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V = R + γPV

Kernels

• Properties:– Symmetric function between two points:– PSD K-matrix

• Uses:– Dot-product in high-dimensional space (kernel trick)– Gain expressiveness

• Risks:– Overfitting– High computational cost

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k(si,s j )

Outline

• Motivation, Notation, and Framework• Kernel-Based Models

– Model-Based VFA– Interpretation of Previous Work

• Bellman Error Decomposition• Experimental Results and Conclusions

Kernelized Regression

• Apply kernel trick to least-squares regression

• t: target values• K: kernel matrix, where• k(x): column vector, where• : regularization matrix

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y(x) = k(x)T (K + Σ)−1t

€

Σ€

K ij = k(si,s j )

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ki(x) = k(si,x)

Kernel-Based Models

• Approximate reward model

• Approximate transition model– Want to predict k(s’) (not s’)– Construct matrix K’, where€

ˆ R (s) = k(s)T (K + ΣR )−1r

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ˆ k (s') = k(s)T (K + ΣP )−1K '

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′ K ij = k( ′ s i,s j )

Samples

Model VFA

Model-based Value Function

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ˆ V (s) = ˆ R (s) + γ ˆ R (s') + γ 2 ˆ R (s' ') + ...

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=k(s)T K − γK '( )−1

r

€

ˆ R (s) = k(s)T (K + ΣR )−1r

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ˆ k (s') = k(s)T (K + ΣP )−1K '

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=k(s)T K−1r + γk(s')T K−1r + γ 2k(s' ')T K−1r + ...

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ˆ R (s)

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ˆ R ( ′ s )

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=k(s)T K−1r + γk(s)T K−1K 'K−1r + γ 2k(s)T (K−1K ')2K−1r + ...

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ˆ k (s')

Samples

Model VFA

Model-based Value Function

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ˆ V (s) = k(s)T K + ΣR( ) + γ K + ΣR( ) K + ΣP( )−1

′ K [ ]−1

r€

ˆ V (s) = k(s)T K − γK '( )−1

r

€

ˆ V = K K + ΣR( ) + γ K + ΣR( ) K + ΣP( )−1

′ K [ ]−1

r

€

wSamples

Model VFA

Unregularized:

Regularized:

Whole state space:

Previous Work

• Kernel Least-Squares Temporal Difference Learning (KLSTD) [Xu et. al., 2005]

– Rederive LSTD, replacing dot products with kernels– No regularization

• Gaussian Process Temporal Difference Learning (GPTD) [Engel, et al., 2005]

– Model value directly with a GP• Gaussian Processes in Reinforcement Learning (GPRL)

[Rasmussen and Kuss, 2004]

– Model transitions and value with GPs– Deterministic rewardSamples

Model VFA

EquivalencyMethod Value Function Model-based

Equivalent

KLSTDGPTDGPRLModel-based [T&P `09]

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w = KHK( )−1

Kr

€

w = HT HKHT + Σ( )−1

r

€

w = K + σ 2Δ − γ ′ K ( )−1

r

€

w = K + ΣR( ) + γ K + ΣR( ) K + ΣP( )−1

′ K [ ]−1

r

€

H = I − γPΣ

σ 2Δ

: GPTD noise parameter

: GPRL regularization parameter

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ΣP = ΣR = 0

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ΣP = ΣR = Σ(HT )−1

€

ΣP = ΣR = σ 2Δ

Samples

Model VFA

Outline

• Motivation, Notation, and Framework• Kernel-Based Models

– Model-Based VFA– Interpretation of Previous Work

• Bellman Error Decomposition• Experimental Results and Conclusions

Model Error

• Error in reward approximation:

• Error in transition approximation:€

ΔR = R − ˆ R

= R − K(K + ΣR )−1r

€

Δ ′ K = PK − ˆ P K

= ′ K − ˆ P K

= ′ K − K(K + ΣP )−1 ′ K

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′ K ij = E k( ′ s i,s j )[ ]

€

PKˆ P K

: expected next kernel values

: approximate next kernel values

Bellman Error

€

BE(Kw) = ΔR + γΔK 'w

rewarderror

transitionerror

€

ΔR = R − ˆ R

Δ ′ K = PK − ˆ P K

Bellman Error a linear combination of reward and transition errors

Outline

• Motivation, Notation, and Framework• Kernel-Based Models

– Model-Based VFA– Interpretation of Previous Work

• Bellman Error Decomposition• Experimental Results and Conclusions

Experiments

• Version of two room problem [Mahadevan & Maggioni, 2006]

• Use Bellman Error decomposition to tune regularization parameters

REWAR

D

Experiments

€

ΣP = 0 ΣR = 0

€

ΣP = 0.1I ΣR = 0

€

ˆ V

€

BE

€

Δ ′ K w

€

ΔR

Conclusion

• Novel, model-based view of kernelized RL built around kernel regression

• Previous work differs from model-based view only in approach to regularization

• Bellman Error can be decomposed into transition and reward error

• Transition and reward error can be used to tune parameters

Thank you!

What about policy improvement?

• Wrap policy iteration around kernelized VFA– Example: KLSPI– Bellman error decomposition will be policy

dependent– Choice of regularization parameters may be

policy dependent• Our results do not apply to SARSA variants

of kernelized RL, e.g., GPSARSA

What’s left?

• Kernel selection– Kernel selection (not just parameter tuning)– Varying kernel parameters across states– Combining kernels (See Kolter & Ng ‘09)

• Computation costs in large problems– K is O(#samples)– Inverting K is expensive– Role of sparsification, interaction w/regularization

Comparing model-based approaches

• Transition model– GPRL: models s’ as a GP– T&P: approximates k(s’) given k(s)

• Reward model– GPRL: deterministic reward– T&P: reward approximated with regularized,

kernelized regression

Don’t you have to know the model?

• For our experiments & graphs: Reward, transition errors calculated with true R, K’

• In practice: Cross-validation could be used to tune parameters to minimize reward and transition errors

Why is the GPTD regularization term asymmetric?

• GPTD is equivalent to T&P when• Can be viewed as propagating the regularizer

through the transition model– – Is this a good idea?– Our contribution: Tools to evaluate this question

€

ΣP = ΣR = Σ(HT )−1

iT

i

iT PH )()(0

1 ∑∞

=

− =

What about Variances?

• Variances can play an important role in Bayesian interpretations of kernelized RL– Can guide exploration– Can ground regularization parameters

• Our analysis focuses on the mean• Variances a valid topic for future work

Does this apply to the recent work of Farahmand et al.?

• Not directly• All methods assume (s,r,s’) data• Farahmand et al. include next states (s’’) in

their kernel, i.e., k(s’’,s) and k(s’’,s’)• Previous work, and ours, includes only s’ in

the kernel: k(s’,s)

How is This Different from Parr et al. ICML 2008?

• Parr et al. considers linear fixed point solutions, not kernelized methods

• Equivalence between linear fixed point methods was fairly well understood already

• Our contribution:– We provide a unifying view of previous kernel-based methods– We extend the equivalence between model-based and direct

methods to the kernelized case