We consider the reinforcement learning problem for the constrained Markov decision process (CMDP), which plays a central role in satisfying safety or resource constraints in sequential learning and decision-making.

Our tight mechanism-specific bounds outperform tight mechanism-agnostic bounds and classic group privacy results.

Visual perception plays a critical role in obtaining external information.