Year: 2025
Type: article
Abstract: We consider a control problem for a finite-state Markov system whose performance is evaluated by a coherent Markov risk measure. For each policy, the risk of a state is approximated by a function of its features, thus leading to a lower-dimensional policy evaluation problem, which involves non-differentiable stochastic operators. We introduce mini-batch transition risk mappings, which are particularly suited to our approach, and we use them to derive a robust learning algorithm for Markov policy evaluation. Finally, we discuss structured policy improvement in the feature-based risk-averse setting. The considerations are illustrated with an underwater robot navigation problem in which several waypoints must be visited and the observation results must be reported from selected transmission locations. We identify the relevant features, we test the simulation-based learning method, and we optimize a structured policy in a hyperspace containing all problems with the same number of relevant points.
Source: Annals of Operations Research
Authors: Andrzej Ruszczyński, Shangzhe Yang
Institution: Rutgers, The State University of New Jersey
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,090
Funder: Office of Naval Research
Award: G3617142002
OpenAlex ID: W4413914922