Information-theory based policies for learning in (embodied) closed sensori-motor loops

Over the last two decades great progress has been made in understanding how sensory representations are learned in the brain driven by the principle of efficient coding. In contrast, we are still lacking theories of learning in closed sensor-motor loops is still lacking. My lecture will first review foundational work that defined information gain and proposed it for guiding optimal experimental design and for driving learning in action-perception loops. Second I will present more recent work using information gain for describing exploratory learning of agents in unknown environments combining optimizing information gain within a multi-step time horizon. Finally I will discuss the extension of this work to the exploration of unbounded state spaces.