kernelized value function approximation for reinforcement learning
DESCRIPTION
Kernelized Value Function Approximation for Reinforcement Learning. Gavin Taylor and Ronald Parr Duke University. Kernel: k(s,s’) Training Data: (s,r,s’),(s,r,s’) (s,r,s’)…. Solve for value directly using KLSTD or GPTD. Solve for model as in GPRL. Kernelized Value Function. Kernelized - PowerPoint PPT PresentationTRANSCRIPT
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
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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
€
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 )
€
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' ') + ...
€
=k(s)T K − γK '( )−1
r
€
ˆ R (s) = k(s)T (K + ΣR )−1r
€
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]
€
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
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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
€
′ 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
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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
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ΣP = 0 ΣR = 0
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ΣP = 0.1I ΣR = 0
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ˆ V
€
BE
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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