Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning from Scarce Experience   Searching the space of policies directly for the optimal policy has been one
popular method for solving partially observable reinforcement learning
problems. Typically, with each change of the target policy, its value is
estimated from the results of following that very policy. This requires a large
number of interactions with the environment as different polices are
considered. We present a family of algorithms based on likelihood ratio
estimation that use data gathered when executing one policy (or collection of
policies) to estimate the value of a different policy. The algorithms combine
estimation and optimization stages. The former utilizes experience to build a
non-parametric representation of an optimized function. The latter performs
optimization on this estimate. We show positive empirical results and provide
the sample complexity bound.

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The categories of this paper are: econ
30
The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-ph
19
The categories of this paper are: eess
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-th
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The categories of this paper are: nucl-th
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The categories of this paper are: astro-ph
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The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph