problems has begun. Application of DRL in robotics has emerged as an important area [@Lillicrap2016ContinuousCW]. In particular, it has become an ongoing research effort to improve algorithms such that they can learn from smaller numbers of training examples. But such basic DRL approaches suffer from a general shortcoming: DRL is narrowing down towards a single solution. This makes it vulnerable when objective functions show local optima as is often the case. For exploration-based approaches these may give rise to dead-ends from a general strategic viewpoint as they provide no systematic information on how to further improve beyond the currently attained performance level. This means, in order to come closer to a goal state, it might be necessary to first admit a drop in the objective function that only later-on leads to an increased performance. But, unfortunately, this leads back to the problem of how to direct search as we canÕt do an exhaustive search for complex problem spaces. In the optimization literature such objective functions are called deceptive [@Goldberg:1987a] which has been summarized by Lehman and Stanley [@lehman_abandoning_2011]: “a problem is deceptive if lower-order building blocks, when combined, do not lead to a global optimum.” The general notion of a 