Leveraging reinforcement learning algorithms to control dynamical systems has become an increasingly popular approach over the past years. An important difference between dynamical systems and, for instance, gaming environments, is that failures in dynamical systems are often critical. While a game can simply be restarted, failures in dynamical systems often result in damaging expensive hardware. Thus, algorithms have emerged that guarantee, with high probability, that the system will not incur in any failures during exploration. In this talk, I will present two recent approaches that fall into this category. In exchange for their guarantees, safe learning algorithms can often only explore locally around an initially given safe policy. That way, they may fail to find the global optimum. To address this, I present a recent approach that allows for global exploration while retaining probabilistic safety guarantees. Second, most algorithms focus on regression from continuous sensor inputs to actions of the system. In reality, system dynamics are often affected by discrete “context” variables, such as whether the surface is frozen or wet, which they cannot measure directly. Thus, I present an approach for multi-class classification that provides frequentist guarantees and, therefore, can be used to classify discrete contexts in safe learning algorithms while still providing probabilistic guarantees. Apart from theoretical guarantees, I also show results from hardware experiments for both approaches.