Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Inverse Method for Policy-Iteration Based Algorithms   We present an extension of two policy-iteration based algorithms on weighted
graphs (viz., Markov Decision Problems and Max-Plus Algebras). This extension
allows us to solve the following inverse problem: considering the weights of
the graph to be unknown constants or parameters, we suppose that a reference
instantiation of those weights is given, and we aim at computing a constraint
on the parameters under which an optimal policy for the reference instantiation
is still optimal. The original algorithm is thus guaranteed to behave well
around the reference instantiation, which provides us with some criteria of
robustness. We present an application of both methods to simple examples. A
prototype implementation has been done.

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The categories of this paper are: math
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
6
The categories of this paper are: astro-ph
33
The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci
19
The categories of this paper are: eess
28
The categories of this paper are: alg-geom
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The categories of this paper are: q-fin
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The categories of this paper are: physics
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The categories of this paper are: hep-ex
20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio