  In this paper, we study the problem of learning safe control policies that
are also effective -- i.e., maximizing the probability of satisfying the linear
temporal logic (LTL) specification of the task, and the discounted reward
capturing the (classic) control performance. We consider unknown environments
that can be modeled as Markov decision processes (MDPs). We propose a
model-free reinforcement learning algorithm that learns a policy that first
maximizes the probability of ensuring the safety, then the probability of
satisfying the given LTL specification and lastly, the sum of discounted
Quality of Control (QoC) rewards. Finally, we illustrate the applicability of
our RL-based approach on a case study.
