In Reinforcement Learning, Intrinsic Motivation motivates directed behaviors through a wide range of reward-generating methods. Depending on the task and environment, these rewards can be useful, might complement each other, but can also break down entirely, as seen with the noisy TV problem for curiosity. We therefore argue that scalability and robustness, among others, are key desirable properties of a method to incorporate intrinsic rewards, which a simple weighted sum of reward lacks. In a t…