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DynaDojo: An Extensible Platform for Benchmarking Scaling in Dynamical System Identification

Logan M Bhamidipaty · Tommy Bruzzese · Caryn Tran · Rami Ratl Mrad · Maxinder S. Kanwal

Great Hall & Hall B1+B2 (level 1) #1002
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[ Paper [ Poster [ OpenReview
Tue 12 Dec 8:45 a.m. PST — 10:45 a.m. PST


Modeling complex dynamical systems poses significant challenges, with traditional methods struggling to work on a variety of systems and scale to high-dimensional dynamics. In response, we present DynaDojo, a novel benchmarking platform designed for data-driven dynamical system identification. DynaDojo provides diagnostics on three ways an algorithm’s performance scales: across the number of training samples, the complexity of a dynamical system, and a target error to achieve. Furthermore, DynaDojo enables studying out-of-distribution generalization (by providing unique test conditions for each system) and active learning (by supporting closed-loop control). Through its user-friendly and easily extensible API, DynaDojo accommodates a wide range of user-defined \texttt{Algorithms}, \texttt{Systems}, and \texttt{Challenges} (evaluation metrics). The platform also prioritizes resource-efficient training with parallel processing strategies for running on a cluster. To showcase its utility, in DynaDojo 0.9, we include implementations of 7 baseline algorithms and 20 dynamical systems, along with several demos exhibiting insights researchers can glean using our platform. This work aspires to make DynaDojo a unifying benchmarking platform for system identification, paralleling the role of OpenAI’s Gym in reinforcement learning.

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