Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generalized Second Order Value Iteration in Markov Decision Processes   Value iteration is a fixed point iteration technique utilized to obtain the
optimal value function and policy in a discounted reward Markov Decision
Process (MDP). Here, a contraction operator is constructed and applied
repeatedly to arrive at the optimal solution. Value iteration is a first order
method and therefore it may take a large number of iterations to converge to
the optimal solution. Successive relaxation is a popular technique that can be
applied to solve a fixed point equation. It has been shown in the literature
that, under a special structure of the MDP, successive over-relaxation
technique computes the optimal value function faster than standard value
iteration. In this work, we propose a second order value iteration procedure
that is obtained by applying the Newton-Raphson method to the successive
relaxation value iteration scheme. We prove the global convergence of our
algorithm to the optimal solution asymptotically and show the second order
convergence. Through experiments, we demonstrate the effectiveness of our
proposed approach.

17
The categories of this paper are: q-fin
5
The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th
26
The categories of this paper are: bayes-an
30
The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol
19
The categories of this paper are: eess
6
The categories of this paper are: astro-ph
24
The categories of this paper are: dg-ga
2
The categories of this paper are: math
8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph