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Poster
Myriad: a real-world testbed to bridge trajectory optimization and deep learning
Nikolaus Howe · Simon Dufort-Labbé · Nitarshan Rajkumar · Pierre-Luc Bacon

Tue Nov 29 09:00 AM -- 11:00 AM (PST) @ Hall J #1011

We present Myriad, a testbed written in JAX which enables machine learning researchers to benchmark imitation learning and reinforcement learning algorithms against trajectory optimization-based methods in real-world environments. Myriad contains 17 optimal control problems presented in continuous time which span medicine, ecology, epidemiology, and engineering. As such, Myriad strives to serve as a stepping stone towards application of modern machine learning techniques for impactful real-world tasks. The repository also provides machine learning practitioners access to trajectory optimization techniques, not only for standalone use, but also for integration within a typical automatic differentiation workflow. Indeed, the combination of classical control theory and deep learning in a fully GPU-compatible package unlocks potential for new algorithms to arise. We present one such novel approach for use in dynamics 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 unknown environment dynamics. All environments, optimizers and tools are available in the software package at \url{https://github.com/nikihowe/myriad}.

Author Information

Nikolaus Howe (Mila, Université de Montréal)
Simon Dufort-Labbé (Montreal Institute for Learning Algorithms, University of Montreal, Université de Montréal)
Nitarshan Rajkumar (University of Cambridge)
Pierre-Luc Bacon (Stanford University)

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