  Historically, artificial intelligence has drawn much inspiration from
neuroscience to fuel advances in the field. However, current progress in
reinforcement learning is largely focused on benchmark problems that fail to
capture many of the aspects that are of interest in neuroscience today. We
illustrate this point by extending a T-maze task from neuroscience for use with
reinforcement learning algorithms, and show that state-of-the-art algorithms
are not capable of solving this problem. Finally, we point out where insights
from neuroscience could help explain some of the issues encountered.
