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Nov, 2020
神经网络策略中实施鲁棒控制保证
Enforcing robust control guarantees within neural network policies
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Priya L. Donti, Melrose Roderick, Mahyar Fazlyab, J. Zico Kolter
TL;DR
通过将人工神经网络用于构建通用非线性控制策略并结合凸优化投影层,本论文提出了一种能在保持鲁棒性的同时提高控制系统的平均性能的技术,同时在非鲁棒的深度强化学习(deep RL)方法的最坏情况稳定性方面也有所提高。
Abstract
When designing controllers for safety-critical systems, practitioners often face a challenging tradeoff between robustness and
performance
. While
robust control
methods provide rigorous guarantees on system stabi
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