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Adversarial Reinforcement Learning for Observer Design in Autonomous Systems under Cyber Attacks. (arXiv:1809.06784v1 [cs.LG])
来源于:arXiv
Complex autonomous control systems are subjected to sensor failures,
cyber-attacks, sensor noise, communication channel failures, etc. that
introduce errors in the measurements. The corrupted information, if used for
making decisions, can lead to degraded performance. We develop a framework for
using adversarial deep reinforcement learning to design observer strategies
that are robust to adversarial errors in information channels. We further show
through simulation studies that the learned observation strategies perform
remarkably well when the adversary's injected errors are bounded in some sense.
We use neural network as function approximator in our studies with the
understanding that any other suitable function approximating class can be used
within our framework. 查看全文>>