Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Optimistic Initialization and Greediness Lead to Polynomial Time
  Learning in Factored MDPs - Extended Version   In this paper we propose an algorithm for polynomial-time reinforcement
learning in factored Markov decision processes (FMDPs). The factored optimistic
initial model (FOIM) algorithm, maintains an empirical model of the FMDP in a
conventional way, and always follows a greedy policy with respect to its model.
The only trick of the algorithm is that the model is initialized
optimistically. We prove that with suitable initialization (i) FOIM converges
to the fixed point of approximate value iteration (AVI); (ii) the number of
steps when the agent makes non-near-optimal decisions (with respect to the
solution of AVI) is polynomial in all relevant quantities; (iii) the per-step
costs of the algorithm are also polynomial. To our best knowledge, FOIM is the
first algorithm with these properties. This extended version contains the
rigorous proofs of the main theorem. A version of this paper appeared in
ICML'09.

37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
26
The categories of this paper are: bayes-an
13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
16
The categories of this paper are: stat
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
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The categories of this paper are: q-fin
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The categories of this paper are: hep-th
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The categories of this paper are: hep-ex
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The categories of this paper are: astro-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: patt-sol