  In addressing control problems such as regulation and tracking through
reinforcement learning, it is often required to guarantee that the acquired
policy meets essential performance and stability criteria such as a desired
settling time and steady-state error prior to deployment. Motivated by this
necessity, we present a set of results and a systematic reward shaping
procedure that (i) ensures the optimal policy generates trajectories that align
with specified control requirements and (ii) allows to assess whether any given
policy satisfies them. We validate our approach through comprehensive numerical
experiments conducted in two representative environments from OpenAI Gym: the
Inverted Pendulum swing-up problem and the Lunar Lander. Utilizing both tabular
and deep reinforcement learning methods, our experiments consistently affirm
the efficacy of our proposed framework, highlighting its effectiveness in
ensuring policy adherence to the prescribed control requirements.
