  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.
