  Recent advances in artificial intelligence have been strongly driven by the
use of game environments for training and evaluating agents. Games are often
accessible and versatile, with well-defined state-transitions and goals
allowing for intensive training and experimentation. However, agents trained in
a particular environment are usually tested on the same or slightly varied
distributions, and solutions do not necessarily imply any understanding. If we
want AI systems that can model and understand their environment, we need
environments that explicitly test for this. Inspired by the extensive
literature on animal cognition, we present an environment that keeps all the
positive elements of standard gaming environments, but is explicitly designed
for the testing of animal-like artificial cognition.
