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.
翻译:我们展示了本地不同物理和机器人环境的开放源码图书馆,配有基于梯度的控制方法和基准套件。引入的环境允许通过模拟动态进行自动差异,从而可以对控制器进行快速培训。图书馆有几种受欢迎的环境,包括OpenAI Gym的古典控制设置。我们还提供了基于深层神经网络的新型差异环境,以模拟医疗通风。我们提供了利用图书馆获得的新科学结果的几种使用案例。其中包括医疗通风机模拟器和控制器、时间变化线形动态系统的适应性控制方法,以及具有对抗干扰的线形动态系统控制新梯度方法。