  Online safe reinforcement learning (RL) involves training a policy that
maximizes task efficiency while satisfying constraints via interacting with the
environments. In this paper, our focus lies in addressing the complex
challenges associated with solving multi-constraint (MC) safe RL problems. We
approach the safe RL problem from the perspective of Multi-Objective
Optimization (MOO) and propose a unified framework designed for MC safe RL
algorithms. This framework highlights the manipulation of gradients derived
from constraints. Leveraging insights from this framework and recognizing the
significance of \textit{redundant} and \textit{conflicting} constraint
conditions, we introduce the Gradient Shaping (GradS) method for general
Lagrangian-based safe RL algorithms to improve the training efficiency in terms
of both reward and constraint satisfaction. Our extensive experimentation
demonstrates the effectiveness of our proposed method in encouraging
exploration and learning a policy that improves both safety and reward
performance across various challenging MC safe RL tasks as well as good
scalability to the number of constraints.
