Using Unity to Help Solve Reinforcement Learning

Leveraging the depth and flexibility of XLand as well as the rapid prototyping features of the Unity engine, we present the United Unity Universe, an open-source toolkit designed to accelerate the creation of innovative reinforcement learning environments. This toolkit includes a robust implementation of OpenXLand, a framework for meta-RL based on XLand 2.0 [23], complemented by a user-friendly interface which allows users to modify the details of procedurally generated terrains and task rules with ease. Along with a ready-to-use implementation of OpenXLand, we provide a curated selection of terrains and rule sets, accompanied by implementations of reinforcement learning baselines to facilitate quick experimentation with novel architectural designs for adaptive agents. Furthermore, we illustrate how the United Unity Universe serves as a high-level language that enables researchers to develop diverse and endlessly variable 3D environments within a unified framework. This functionality establishes the United Unity Universe (U3) as an essential tool for advancing the field of reinforcement learning, especially in the development of adaptive and generalizable learning systems. * Equal contribution. † Equal contribution. 1 https://unity.com/products/unity-personal 38th Conference on Neural Information Processing Systems (NeurIPS 2024) Track on Datasets and Benchmarks.