  There have been numerous advances in reinforcement learning, but the
typically unconstrained exploration of the learning process prevents the
adoption of these methods in many safety critical applications. Recent work in
safe reinforcement learning uses idealized models to achieve their guarantees,
but these models do not easily accommodate the stochasticity or
high-dimensionality of real world systems. We investigate how prediction
provides a general and intuitive framework to constraint exploration, and show
how it can be used to safely learn intersection handling behaviors on an
autonomous vehicle.
