  Safety is a critical feature of controller design for physical systems. When
designing control policies, several approaches to guarantee this aspect of
autonomy have been proposed, such as robust controllers or control barrier
functions. However, these solutions strongly rely on the model of the system
being available to the designer. As a parallel development, reinforcement
learning provides model-agnostic control solutions but in general, it lacks the
theoretical guarantees required for safety. Recent advances show that under
mild conditions, control policies can be learned via reinforcement learning,
which can be guaranteed to be safe by imposing these requirements as
constraints of an optimization problem. However, to transfer from learning
safety to learning safely, there are two hurdles that need to be overcome: (i)
it has to be possible to learn the policy without having to re-initialize the
system; and (ii) the rollouts of the system need to be in themselves safe. In
this paper, we tackle the first issue, proposing an algorithm capable of
operating in the continuing task setting without the need of restarts. We
evaluate our approach in a numerical example, which shows the capabilities of
the proposed approach in learning safe policies via safe exploration.
