Deluca -- A Differentiable Control Library: Environments, Methods, and Benchmarking

Paula Gradu; John Hallman; Daniel Suo; Alex Yu; Naman Agarwal; Udaya Ghai; Karan Singh; Cyril Zhang; Anirudha Majumdar; Elad Hazan

arXiv · 2102.09968

Deluca -- A Differentiable Control Library: Environments, Methods, and Benchmarking

Paula Gradu·John Hallman·Daniel Suo·Alex Yu·Naman Agarwal·Udaya Ghai·Karan Singh·Cyril Zhang·Anirudha Majumdar·Elad Hazan

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Abstract

We present an open-source library of natively differentiable physics and robotics environments, accompanied by gradient-based control methods and a benchmark-ing suite. The introduced environments allow auto-differentiation through the simulation dynamics, and thereby permit fast training of controllers. The library features several popular environments, including classical control settings from OpenAI Gym. We also provide a novel differentiable environment, based on deep neural networks, that simulates medical ventilation. We give several use-cases of new scientific results obtained using the library. This includes a medical ventilator simulator and controller, an adaptive control method for time-varying linear dynamical systems, and new gradient-based methods for control of linear dynamical systems with adversarial perturbations.

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Paula Gradu, John Hallman, Daniel Suo, Alex Yu, Naman Agarwal, Udaya Ghai, Karan Singh, Cyril Zhang, Anirudha Majumdar, Elad Hazan. 2021-02-19. Deluca -- A Differentiable Control Library: Environments, Methods, and Benchmarking. https://arxiv.org/abs/2102.09968

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