Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Value Function Approximation in Noisy Environments Using Locally
  Smoothed Regularized Approximate Linear Programs   Recently, Petrik et al. demonstrated that L1Regularized Approximate Linear
Programming (RALP) could produce value functions and policies which compared
favorably to established linear value function approximation techniques like
LSPI. RALP's success primarily stems from the ability to solve the feature
selection and value function approximation steps simultaneously. RALP's
performance guarantees become looser if sampled next states are used. For very
noisy domains, RALP requires an accurate model rather than samples, which can
be unrealistic in some practical scenarios. In this paper, we demonstrate this
weakness, and then introduce Locally Smoothed L1-Regularized Approximate Linear
Programming (LS-RALP). We demonstrate that LS-RALP mitigates inaccuracies
stemming from noise even without an accurate model. We show that, given some
smoothness assumptions, as the number of samples increases, error from noise
approaches zero, and provide experimental examples of LS-RALP's success on
common reinforcement learning benchmark problems.

29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
2
The categories of this paper are: math
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
3
The categories of this paper are: physics
22
The categories of this paper are: chao-dyn
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The categories of this paper are: econ
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The categories of this paper are: acc-phys
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The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat