  \begin{abstract} We model individual T2DM patient blood glucose level (BGL)
by stochastic process with discrete number of states mainly but not solely
governed by medication regimen (e.g. insulin injections). BGL states change
otherwise according to various physiological triggers which render a
stochastic, statistically unknown, yet assumed to be quasi-stationary, nature
of the process. In order to express incentive for being in desired healthy BGL
we heuristically define a reward function which returns positive values for
desirable BG levels and negative values for undesirable BG levels. The state
space consists of sufficient number of states in order to allow for memoryless
assumption. This, in turn, allows to formulate Markov Decision Process (MDP),
with an objective to maximize the total reward, summarized over a long run. The
probability law is found by model-based reinforcement learning (RL) and the
optimal insulin treatment policy is retrieved from MDP solution.
