  Safety is a critical concern when deploying reinforcement learning agents for
realistic tasks. Recently, safe reinforcement learning algorithms have been
developed to optimize the agent's performance while avoiding violations of
safety constraints. However, few studies have addressed the non-stationary
disturbances in the environments, which may cause catastrophic outcomes. In
this paper, we propose the context-aware safe reinforcement learning (CASRL)
method, a meta-learning framework to realize safe adaptation in non-stationary
environments. We use a probabilistic latent variable model to achieve fast
inference of the posterior environment transition distribution given the
context data. Safety constraints are then evaluated with uncertainty-aware
trajectory sampling. The high cost of safety violations leads to the rareness
of unsafe records in the dataset. We address this issue by enabling prioritized
sampling during model training and formulating prior safety constraints with
domain knowledge during constrained planning. The algorithm is evaluated in
realistic safety-critical environments with non-stationary disturbances.
Results show that the proposed algorithm significantly outperforms existing
baselines in terms of safety and robustness.
