We present Myriad, a testbed written in JAX for learning and planning in real-world continuous environments. The primary contributions of Myriad are threefold. First, Myriad provides machine learning practitioners access to trajectory optimization techniques for application within a typical automatic differentiation workflow. Second, Myriad presents many real-world optimal control problems, ranging from biology to medicine to engineering, for use by the machine learning community. Formulated in continuous space and time, these environments retain some of the complexity of real-world systems often abstracted away by standard benchmarks. As such, Myriad strives to serve as a stepping stone towards application of modern machine learning techniques for impactful real-world tasks. Finally, we use the Myriad repository to showcase a novel approach for learning and control tasks. Trained in a fully end-to-end fashion, our model leverages an implicit planning module over neural ordinary differential equations, enabling simultaneous learning and planning with complex environment dynamics.

翻译：我们提出Myriad——一个基于JAX构建的测试平台，专为真实世界连续环境中的学习与规划任务而设计。Myriad的主要贡献体现在三个方面。首先，它为机器学习从业者提供了在典型自动微分工作流中应用轨迹优化技术的入口。其次，Myriad呈现了涵盖生物学、医学到工程学等多个领域的真实世界最优控制问题，供机器学习社区使用。这些环境在连续空间与时间维度上构建，保留了真实世界系统中常被标准基准测试所抽象化的部分复杂性。因此，Myriad致力于成为推动现代机器学习技术应用于具有影响力的真实世界任务的阶梯。最后，我们利用Myriad代码库展示了一种新颖的学习与控制方法。该模型采用完全端到端训练方式，通过基于神经常微分方程的隐式规划模块，使其能够在复杂环境动力学中同步实现学习与规划。