rodents (where circuit-level mechanisms can be studied experimentally) to humans (where at least some coarse-grain insights into brain activities are possible by neuroimaging techniques).

In the following, we turn towards a particularly illustrative example of how current state-of-the-art approaches can successfully solve the complex problem of six-legged robot walking through the introduction of a task-specific behavioral space, but also dramatically fail upon even slight changes in task demands due to lack of fluid adaptivity seen in biological systems. We will then show that biologically inspired approaches that are based on emergent behavior of interacting modules provide a viable solution to such a dynamic problem. In the end, we want to discuss how insights from this and other biologically inspired approaches could help us in designing solutions with similar fluid adaptivity for artificial intelligent systems.

Approaching Deceptive Problems
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Over the last couple of years, deep reinforcement learning (DRL) has been successfully applied in the area of computer games [@mnih_human-level_2015]. This area provides a suitable testbed as it allows the learning algorithm to sample a large number of training examples in order to generate good estimates. As more advanced algorithms have been developed, a transfer of such approaches to more real-world 