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.
@article{arxiv.1809.02627,
title = {Unity: A General Platform for Intelligent Agents},
author = {Arthur Juliani and Vincent-Pierre Berges and Ervin Teng and Andrew Cohen and Jonathan Harper and Chris Elion and Chris Goy and Yuan Gao and Hunter Henry and Marwan Mattar and Danny Lange},
journal= {arXiv preprint arXiv:1809.02627},
year = {2020}
}