This paper introduces ResidencyRL, a novel reinforcement learning framework designed to train clinical AI agents through extensive simulated multi-turn patient encounters. The method significantly improves diagnostic accuracy, management quality, and patient-centered communication, demonstrating robust and generalizable capabilities for sequential clinical decision-making.