  The growing trend of fledgling reinforcement learning systems making their
way into real-world applications has been accompanied by growing concerns for
their safety and robustness. In recent years, a variety of approaches have been
put forward to address the challenges of safety-aware reinforcement learning;
however, these methods often either require a handcrafted model of the
environment to be provided beforehand, or that the environment is relatively
simple and low-dimensional. We present a novel approach to safety-aware deep
reinforcement learning in high-dimensional environments called latent
shielding. Latent shielding leverages internal representations of the
environment learnt by model-based agents to "imagine" future trajectories and
avoid those deemed unsafe. We experimentally demonstrate that this approach
leads to improved adherence to formally-defined safety specifications.
