  Plain reinforcement learning (RL) may be prone to loss of convergence,
constraint violation, unexpected performance, etc. Commonly, RL agents undergo
extensive learning stages to achieve acceptable functionality. This is in
contrast to classical control algorithms which are typically model-based. An
direction of research is the fusion of RL with such algorithms, especially
model-predictive control (MPC). This, however, introduces new hyper-parameters
related to the prediction horizon. Furthermore, RL is usually concerned with
Markov decision processes. But the most of the real environments are not
time-discrete. The factual physical setting of RL consists of a digital agent
and a time-continuous dynamical system. There is thus, in fact, yet another
hyper-parameter -- the agent sampling time. In this paper, we investigate the
effects of prediction horizon and sampling of two hybrid RL-MPC-agents in a
case study with a mobile robot parking, which is in turn a canonical control
problem. We benchmark the agents with a simple variant of MPC. The sampling
showed a kind of a "sweet spot" behavior, whereas the RL agents demonstrated
merits at shorter horizons.
