Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Performance guarantees for model-based Approximate Dynamic Programming
  in continuous spaces   We study both the value function and Q-function formulation of the Linear
Programming approach to Approximate Dynamic Programming. The approach is
model-based and optimizes over a restricted function space to approximate the
value function or Q-function. Working in the discrete time, continuous space
setting, we provide guarantees for the fitting error and online performance of
the policy. In particular, the online performance guarantee is obtained by
analyzing an iterated version of the greedy policy, and the fitting error
guarantee by analyzing an iterated version of the Bellman inequality. These
guarantees complement the existing bounds that appear in the literature. The
Q-function formulation offers benefits, for example, in decentralized
controller design, however it can lead to computationally demanding
optimization problems. To alleviate this drawback, we provide a condition that
simplifies the formulation, resulting in improved computational times.

33
The categories of this paper are: plasm-ph
19
The categories of this paper are: eess
13
The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
32
The categories of this paper are: atom-ph
23
The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph