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Scalable Safe Exploration for Global Optimization of Dynamical Systems
[article]
2022
Learning optimal control policies directly on physical systems is challenging since even a single failure can lead to costly hardware damage. Most existing learning methods that guarantee safety, i.e., no failures, during exploration are limited to local optima. A notable exception is the GoSafe algorithm, which, unfortunately, cannot handle high-dimensional systems and hence cannot be applied to most real-world dynamical systems. This work proposes GoSafeOpt as the first algorithm that can
doi:10.48550/arxiv.2201.09562
fatcat:paulnuq5rfajhkainfippwyska