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.

本文提出了一种基于像素观察安全的强化学习方法，在部分可观察的马尔可夫决策过程框架下，使用一种新颖的安全评估方法来训练安全策略，使用基准数据证明了其相对于现有方法具有竞争性的性能和满足安全约束条件。