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