GoSafeOpt: Scalable safe exploration for global optimization of dynamical systems
Bhavya Sukhija, Matteo Turchetta, David Lindner, Andreas Krause, Sebastian Trimpe, Dominik Baumann
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.
Topics & Concepts
ScalabilityComputer scienceDynamical systems theoryArtificial intelligencePhysicsQuantum mechanicsDatabaseReinforcement Learning in RoboticsMachine Learning and AlgorithmsFault Detection and Control Systems