  Long-run average optimization problems for Markov decision processes (MDPs)
require constructing policies with optimal steady-state behavior, i.e., optimal
limit frequency of visits to the states. However, such policies may suffer from
local instability, i.e., the frequency of states visited in a bounded time
horizon along a run differs significantly from the limit frequency. In this
work, we propose an efficient algorithmic solution to this problem.
