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

• URL: http://arxiv.org/abs/2102.09968v1
• Date: Fri, 19 Feb 2021 15:06:47 GMT
• Title: Deluca -- A Differentiable Control Library: Environments, Methods, and Benchmarking
• Authors: Paula Gradu, John Hallman, Daniel Suo, Alex Yu, Naman Agarwal, Udaya Ghai, Karan Singh, Cyril Zhang, Anirudha Majumdar, Elad Hazan
• Abstract summary: We present an open-source library of differentiable physics and robotics environments. The library features several popular environments, including classical control settings from OpenAI Gym. We give several use-cases of new scientific results obtained using the library.
• Score: 52.44199258132215
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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