Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Reachability analysis in stochastic directed graphs by reinforcement
  learning   We characterize the reachability probabilities in stochastic directed graphs
by means of reinforcement learning methods. In particular, we show that the
dynamics of the transition probabilities in a stochastic digraph can be modeled
via a difference inclusion, which, in turn, can be interpreted as a Markov
decision process. Using the latter framework, we offer a methodology to design
reward functions to provide upper and lower bounds on the reachability
probabilities of a set of nodes for stochastic digraphs. The effectiveness of
the proposed technique is demonstrated by application to the diffusion of
epidemic diseases over time-varying contact networks generated by the proximity
patterns of mobile agents.

0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
2
The categories of this paper are: math
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc
8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
17
The categories of this paper are: q-fin