Safe Time-Varying Optimization based on Gaussian Processes with Spatio-Temporal Kernel
Ensuring safety is a key aspect in sequential decision making problems, such as robotics or process control. The complexity of the underlying systems often makes finding the optimal decision challenging, especially when the safety-critical system is time-varying. Overcoming the problem of optimizing an unknown time-varying reward subject to unknown time-varying safety constraints, we propose TVSafeOpt, a new algorithm built on Bayesian optimization with a spatio-temporal kernel. The algorithm is capable of safely tracking a time-varying safe region without the need for explicit change detection. Optimality guarantees are also provided for the algorithm when the optimization problem becomes stationary. We show that TVSafeOpt compares favorably against SafeOpt on synthetic data, both regarding safety and optimality. Evaluation on a realistic case study with gas compressors confirms that TVSafeOpt ensures safety when solving time-varying optimization problems with unknown reward and safety functions.
Code (0)
등록된 구현이 없습니다.
Tasks
Bayesian OptimizationChange DetectionDecision MakingGaussian ProcessesSequential Decision MakingSimilar Papers 제목 키워드 기반
Future Aware Safe Active Learning of Time Varying Systems using Gaussian Processes
Experimental exploration of high-cost systems with safety constraints, common in engineering applications, is a challenging endeavor. Data-driven models offer a promising solution, but acquiring the requisite data remain…
Active LearningGaussian ProcessesSafe Real-Time Optimization using Multi-Fidelity Gaussian Processes
This paper proposes a new class of real-time optimization schemes to overcome system-model mismatch of uncertain processes. This work's novelty lies in integrating derivative-free optimization schemes and multi-fidelity …
Bayesian OptimizationGaussian ProcessesUncertainty QuantificationSafe and Adaptive Decision-Making for Optimization of Safety-Critical Systems: The ARTEO Algorithm
We consider the problem of decision-making under uncertainty in an environment with safety constraints. Many business and industrial applications rely on real-time optimization to improve key performance indicators. In t…
Decision MakingDecision Making Under UncertaintyGaussian ProcessesMulti-Armed BanditsSafe Learning of Uncertain Environments
In many learning based control methodologies, learning the unknown dynamic model precedes the control phase, while the aim is to control the system such that it remains in some safe region of the state space. In this wor…
4DGT: Learning a 4D Gaussian Transformer Using Real-World Monocular Videos
We propose 4DGT, a 4D Gaussian-based Transformer model for dynamic scene reconstruction, trained entirely on real-world monocular posed videos. Using 4D Gaussian as an inductive bias, 4DGT unifies static and dynamic comp…
Inductive Bias