  We propose a curiosity reward based on information theory principles and
consistent with the animal instinct to maintain certain critical parameters
within a bounded range. Our experimental validation shows the added value of
the additional homeostatic drive to enhance the overall information gain of a
reinforcement learning agent interacting with a complex environment using
continuous actions. Our method builds upon two ideas: i) To take advantage of a
new Bellman-like equation of information gain and ii) to simplify the
computation of the local rewards by avoiding the approximation of complex
distributions over continuous states and actions.
