  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.
