  Recent advances in artificial intelligence have been driven by the presence
of increasingly realistic and complex simulated environments. However, many of
the existing environments provide either unrealistic visuals, inaccurate
physics, low task complexity, restricted agent perspective, or a limited
capacity for interaction among artificial agents. Furthermore, many platforms
lack the ability to flexibly configure the simulation, making the simulated
environment a black-box from the perspective of the learning system. In this
work, we propose a novel taxonomy of existing simulation platforms and discuss
the highest level class of general platforms which enable the development of
learning environments that are rich in visual, physical, task, and social
complexity. We argue that modern game engines are uniquely suited to act as
general platforms and as a case study examine the Unity engine and open source
Unity ML-Agents Toolkit. We then survey the research enabled by Unity and the
Unity ML-Agents Toolkit, discussing the kinds of research a flexible,
interactive and easily configurable general platform can facilitate.
