Intrinsically motivated information foraging

We treat information gathering as a POMDP in which the goal is to maximize an accumulated intrinsic reward at each time step based on the negative entropy of the agent´s beliefs about the world state. We show that such information foraging agents can discover intelligent exploration policies that take into account the long-term effects of sensor and motor actions, and can automatically adapt to variations in sensor noise, different amounts of prior information, and limited memory conditions.