GoSafeOpt: Scalable Safe Exploration for Global Optimization of Dynamical Systems

Bhavya Sukhija, Matteo Turchetta, David Lindner, Andreas Krause, Sebastian Trimpe, Dominik Baumann

Research output: Contribution to journalArticleScientificpeer-review

5 Citations (Scopus)
56 Downloads (Pure)

Abstract

Learning optimal control policies directly on physical systems is challenging. Even a single failure can lead to costly hardware damage. Most existing model-free learning methods that guarantee safety, i.e., no failures, during exploration are limited to local optima. This work proposes GOSAFEOPT as the first provably safe and optimal algorithm that can safely discover globally optimal policies for systems with high-dimensional state space. We demonstrate the superiority of GOSAFEOPT over competing model-free safe learning methods in simulation and hardware experiments on a robot arm.

Original languageEnglish
Article number103922
Number of pages25
JournalArtificial Intelligence
Volume320
Early online date20 Apr 2023
DOIs
Publication statusPublished - Jul 2023
MoE publication typeA1 Journal article-refereed

Keywords

  • Computer Science - Machine Learning
  • Electrical Engineering and Systems Science - Systems and Control

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