A Unified Bellman Operator for Safety-Critical Reinforcement Learning
JointSAC proposes a new method to train AI agents that maximize task performance while strictly following safety rules. It introduces a unified Bellman operator that combines these two goals into one learning process using a two-timescale approach. This allows the agent to find policies that are both highly rewarding and provably safe, even when facing unpredictable disturbances.