Autonomous manipulation of granular media, such as sand, is crucial for applications in construction, excavation, and additive manufacturing.However, shaping granular materials presents unique challenges due to their high-dimensional configuration space and complex dynamics, where traditional rule-based approaches struggle without extensive engineering efforts.Reinforcement learning (RL) offers a promising alternative by enabling agents to learn adaptive manipulation strategies through trial and error.In this work, we present an RL framework that enables a robotic arm with a cubic end-effector and a stereo camera to shape granular media into desired target structures.We show the importance of compact observations and concise reward formulations for the large configuration space, validating our design choices with an ablation study.Our results demonstrate the effectiveness of the proposed approach for the training of visual policies that manipulate granular media including their real-world deployment, outperforming two baseline approaches.