GoSafeOpt: Scalable Safe Exploration for Global Optimization of Dynamical Systems
Learning optimal control policies directly on physical systems is challenging since 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. 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 safely discover globally optimal policies for high-dimensional systems while giving safety and optimality guarantees. We demonstrate the superiority of GoSafeOpt over competing model-free safe learning methods on a robot arm that would be prohibitive for GoSafe.
Code (1)
Tasks
global-optimizationSafe ExplorationSimilar Papers 제목 키워드 기반
Deep Gaussian Process Proximal Policy Optimization
Uncertainty estimation for Reinforcement Learning (RL) is a critical component in control tasks where agents must balance safe exploration and efficient learning. While deep neural networks have enabled breakthroughs in …
Reinforcement LearningContinuous ControlGaussian ProcessesGlobal Safe Sequential Learning via Efficient Knowledge Transfer
Sequential learning methods, such as active learning and Bayesian optimization, aim to select the most informative data for task learning. In many applications, however, data selection is constrained by unknown safety co…
Active LearningBayesian OptimizationComputational EfficiencyGaussian Processes+1Scalable Global Optimization via Local Bayesian Optimization
Bayesian optimization has recently emerged as a popular method for the sample-efficient optimization of expensive black-box functions. However, the application to high-dimensional problems with several thousand observati…
Bayesian Optimizationglobal-optimizationReinforcement LearningChance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems
Learning-based control algorithms require data collection with abundant supervision for training. Safe exploration algorithms ensure the safety of this data collection process even when only partial knowledge is availabl…
Motion PlanningOptimal Motion PlanningSafe ExplorationDynamic Regret Analysis of Safe Distributed Online Optimization for Convex and Non-convex Problems
This paper addresses safe distributed online optimization over an unknown set of linear safety constraints. A network of agents aims at jointly minimizing a global, time-varying function, which is only partially observab…