Learning policies for attentional control

We propose two behaviourally active policies for attentional control. These policies must act based on a multi-modal sensory feedback. Two approaches are used to derive the policies: 1) a simple straightforward strategy, and 2) using Q-learning to learn a policy based on the perceptual state of the system. As a practical result of both algorithms, a robotic agent is capable to select a region of interest and perform shifts of attention focusing on the selected region. Then, a multi-feature extraction can take place allowing the system to identify or recognize a pattern representing that region of interest. Also, the policies have the desired property that all objects in the environment are visited at least once, although some of them can be visited more. In this way a robotic agent can relate sensed information to actions, abstracting and providing a feedback (categorization and mapping) for environmental stimuli