Researchers propose a new method, Sampling-Guided Policy Search (SGPS), to accelerate visual policy learning for AI agents. SGPS combines sampling-based model-predictive control with first-order policy optimization to learn policies for locomotion and manipulation. This approach is shown to improve policy learning and enable zero-shot transfer to real-world robots.