  In the pursuit of artificial general intelligence, our most significant
measurement of progress is an agent's ability to achieve goals in a wide range
of environments. Existing platforms for constructing such environments are
typically constrained by the technologies they are founded on, and are
therefore only able to provide a subset of scenarios necessary to evaluate
progress. To overcome these shortcomings, we present our use of Unity, a widely
recognized and comprehensive game engine, to create more diverse, complex,
virtual simulations. We describe the concepts and components developed to
simplify the authoring of these environments, intended for use predominantly in
the field of reinforcement learning. We also introduce a practical approach to
packaging and re-distributing environments in a way that attempts to improve
the robustness and reproducibility of experiment results. To illustrate the
versatility of our use of Unity compared to other solutions, we highlight
environments already created using our approach from published papers. We hope
that others can draw inspiration from how we adapted Unity to our needs, and
anticipate increasingly varied and complex environments to emerge from our
approach as familiarity grows.
