instance, a change to a different task, or a change in the environment, as they are commonplace in most real-world scenarios. Coping with them requires forms of “fluid” adaptivity — captured in the policy — that can react much quicker than the adaptivity that shapes the skill through the deep learning dynamics (We introduce here the distinction in fluid and crystallized adaptivity that draws on the notion of a distinction in fluid and crystallized intelligence [@cattell1971abilities].).

Even though there are now approaches that start to take into account selected aspects of fluid adaptivity, from our point of view this notion is even broader and has to deal with further sources of variance. Biological agents in the real word can execute previously learned tasks in different environments. This is not accounted for in approaches dealing with classical games, such as Go or Chess, as in these cases the game is explicitly made invariant to the environment and the rules of the game are embedded into the interaction with the environment. However, physical tasks typically depend strongly on the environment. Dynamically coping with such changes requires correspondingly extended levels of fluid adaptivity. Furthermore, the task itself may change: now fluid adaptivity must 