(Ithunyelwe ngomhla ka-1 Ephreli 2020)

Abstract: The Obstacle Tower Challenge is the task to master a procedurally generated
chain of levels that subsequently get harder to complete. Whereas the top 6
performing entries of last year’s competition all used human demonstrations 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.