  Reinforcement learning is commonly associated with training of
reward-maximizing (or cost-minimizing) agents, in other words, controllers. It
can be applied in model-free or model-based fashion, using a priori or online
collected system data to train involved parametric architectures. In general,
online reinforcement learning does not guarantee closed loop stability unless
special measures are taken, for instance, through learning constraints or
tailored training rules. Particularly promising are hybrids of reinforcement
learning with "classical" control approaches. In this work, we suggest a method
to guarantee practical stability of the system-controller closed loop in a
purely online learning setting, i.e., without offline training. Moreover, we
assume only partial knowledge of the system model. To achieve the claimed
results, we employ techniques of classical adaptive control. The implementation
of the overall control scheme is provided explicitly in a digital, sampled
setting. That is, the controller receives the state of the system and computes
the control action at discrete, specifically, equidistant moments in time. The
method is tested in adaptive traction control and cruise control where it
proved to significantly reduce the cost.
