Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Statistical Learning for Probability-Constrained Stochastic Optimal
  Control   We investigate Monte Carlo based algorithms for solving stochastic control
problems with probabilistic constraints. Our motivation comes from microgrid
management, where the controller tries to optimally dispatch a diesel generator
while maintaining low probability of blackouts. The key question we investigate
are empirical simulation procedures for learning the admissible control set
that is specified implicitly through a probability constraint on the system
state. We propose a variety of relevant statistical tools including logistic
regression, Gaussian process regression, quantile regression and support vector
machines, which we then incorporate into an overall Regression Monte Carlo
(RMC) framework for approximate dynamic programming. Our results indicate that
using logistic or Gaussian process regression to estimate the admissibility
probability outperforms the other options. Our algorithms offer an efficient
and reliable extension of RMC to probability-constrained control. We illustrate
our findings with two case studies for the microgrid problem.

11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics
23
The categories of this paper are: solv-int
26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ
9
The categories of this paper are: nlin
1
The categories of this paper are: cs
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc
33
The categories of this paper are: plasm-ph
19
The categories of this paper are: eess