Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learn Quasi-stationary Distributions of Finite State Markov Chain   We propose a reinforcement learning (RL) approach to compute the expression
of quasi-stationary distribution. Based on the fixed-point formulation of
quasi-stationary distribution, we minimize the KL-divergence of two Markovian
path distributions induced by the candidate distribution and the true target
distribution. To solve this challenging minimization problem by gradient
descent, we apply the reinforcement learning technique by introducing the
reward and value functions. We derive the corresponding policy gradient theorem
and design an actor-critic algorithm to learn the optimal solution and the
value function. The numerical examples of finite state Markov chain are tested
to demonstrate the new method.

27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics
16
The categories of this paper are: stat
24
The categories of this paper are: dg-ga
19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc
21
The categories of this paper are: adap-org
30
The categories of this paper are: q-alg
18
The categories of this paper are: econ
29
The categories of this paper are: funct-an
9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio