We propose an architectural model for a responsive vision system based on techniques of reinforcement learning. It is capable of acquiring object representations based on the intended application. The system can be interpreted as an intelligent scanner that interacts with its environment in a perception-action cycle, choosing the camera parameters for the next view of an object depending on the information it has perceived so far. The main contribution of this paper consists in the presentation of this general architecture which can be used for a variety of applications in computer vision and computer graphics. In addition, the funcionality of the system is demonstrated with the example of learning a sparse, view-based object representation that allows for the reconstruction of non-acquired views. First results suggest the usability of the proposed system.