Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Data-driven Distributionally Robust Optimal Stochastic Control Using the
  Wasserstein Metric   Optimal control of a stochastic dynamical system usually requires a good
dynamical model with probability distributions, which is difficult to obtain
due to limited measurements and/or complicated dynamics. To solve it, this work
proposes a data-driven distributionally robust control framework with the
Wasserstein metric via a constrained two-player zero-sum Markov game, where the
adversarial player selects the probability distribution from a Wasserstein ball
centered at an empirical distribution. Then, the game is approached by its
penalized version, an optimal stabilizing solution of which is derived
explicitly in a linear structure under the Riccati-type iterations. Moreover,
we design a model-free Q-learning algorithm with global convergence to learn
the optimal controller. Finally, we verify the effectiveness of the proposed
learning algorithm and demonstrate its robustness to the probability
distribution errors via numerical examples.

10
The categories of this paper are: q-bio
16
The categories of this paper are: stat
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
17
The categories of this paper are: q-fin
12
The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
29
The categories of this paper are: funct-an
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph