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.

Myriad 是一个用 JAX 编写的测试平台，为学习和计划真实连续环境提供了轨迹优化技术，包含多种真实世界最优控制问题，并展示了一个基于神经微分方程的隐式规划模块的全端到端训练方法，以实现对复杂真实世界任务的影响。

Myriad: 一个真实世界的试验平台，用于桥接轨迹优化和深度学习