  In the field of safe reinforcement learning (RL), finding a balance between
satisfying safety constraints and optimizing reward performance presents a
significant challenge. A key obstacle in this endeavor is the estimation of
safety constraints, which is typically more difficult than estimating a reward
metric due to the sparse nature of the constraint signals. To address this
issue, we introduce a novel framework named Feasibility Consistent Safe
Reinforcement Learning (FCSRL). This framework combines representation learning
with feasibility-oriented objectives to identify and extract safety-related
information from the raw state for safe RL. Leveraging self-supervised learning
techniques and a more learnable safety metric, our approach enhances the policy
learning and constraint estimation. Empirical evaluations across a range of
vector-state and image-based tasks demonstrate that our method is capable of
learning a better safety-aware embedding and achieving superior performance
than previous representation learning baselines.
