  The Obstacle Tower Challenge is the task to master a procedurally generated
chain of levels that subsequently get harder to complete. Whereas the most top
performing entries of last year's competition used human demonstrations or
reward shaping to learn how to cope with the challenge, we present an approach
that performed competitively (placed 7th) but starts completely from scratch by
means of Deep Reinforcement Learning with a relatively simple feed-forward deep
network structure. We especially look at the generalization performance of the
taken approach concerning different seeds and various visual themes that have
become available after the competition, and investigate where the agent fails
and why. Note that our approach does not possess a short-term memory like
employing recurrent hidden states. With this work, we hope to contribute to a
better understanding of what is possible with a relatively simple, flexible
solution that can be applied to learning in environments featuring complex 3D
visual input where the abstract task structure itself is still fairly simple.
