also converges towards better overall solutions than many other search algorithms. Such an approach that creates a complex behavior space mapping towards detailed actions was shown to be effective when dealing, for example, with changing morphology. After losing a leg the low dimensional behavior space was used in what they called intelligent trial and error learning: the behavior space was used for variations and different behaviors were tested. The structure of the low dimensional behavior space was used to converge towards a suitable motor control program.

Dynamic Problem Spaces — Fluid Adaptivity in Biological Systems
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The approach by Cully et al. [@cully_robots_2015] has shown to work efficiently for changes of morphology. And this provides an important first step: in real world scenarios, we have to deal with changes of an environmental setting and in particular the environmentÕs response to the actions of an agent might change. This is difficult for all control approaches and in the case of a formalization of reinforcement learning as one holistic agent observing interactions with the environment taken as one singular unit this becomes problematic.

It should be noted that the example of morphology change considers only one singular change. In contrast, many real 