Safe DNN-type Controller Synthesis for Nonlinear Systems via Meta Reinforcement Learning

There is a pressing need to synthesize provable safety controllers for nonlinear systems as they are embedded in many safety-critical applications. In this paper, we propose a safe Meta Reinforcement Learning (Meta-RL) approach to synthesize deep neural network (DNN) controllers for nonlinear system...

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Vydané v:	2023 60th ACM/IEEE Design Automation Conference (DAC) s. 1 - 6
Hlavní autori:	Zhao, Hanrui, Zeng, Xia, Qi, Niuniu, Yang, Zhengfeng, Zeng, Zhenbing
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 09.07.2023
Predmet:	Artificial neural networks Benchmark testing controller synthesis formal verification meta learning Metalearning Pressing Reinforcement learning Safety Training
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