  We address the problem of safe reinforcement learning from pixel
observations. Inherent challenges in such settings are (1) a trade-off between
reward optimization and adhering to safety constraints, (2) partial
observability, and (3) high-dimensional observations. We formalize the problem
in a constrained, partially observable Markov decision process framework, where
an agent obtains distinct reward and safety signals. To address the curse of
dimensionality, we employ a novel safety critic using the stochastic latent
actor-critic (SLAC) approach. The latent variable model predicts rewards and
safety violations, and we use the safety critic to train safe policies. Using
well-known benchmark environments, we demonstrate competitive performance over
existing approaches with respects to computational requirements, final reward
return, and satisfying the safety constraints.
