  We consider the control of a Markov decision process (MDP) that undergoes an
abrupt change in its transition kernel (mode). We formulate the problem of
minimizing regret under control-switching based on mode change detection,
compared to a mode-observing controller, as an optimal stopping problem. Using
a sequence of approximations, we reduce it to a quickest change detection (QCD)
problem with Markovian data, for which we characterize a state-dependent
threshold-type optimal change detection policy. Numerical experiments
illustrate various properties of our control-switching policy.
