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Safe Exploration

Safe Exploration is an approach to collect ground truth data by safely interacting with the environment.

Source: Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems

Papers

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Showing 21–30 of 135 papers

TitleStatusHype
Learning to explore when mistakes are not allowed—0
Learning-Enhanced Safeguard Control for High-Relative-Degree Systems: Robust Optimization under Disturbances and Faults—0
Safe Bayesian Optimization for the Control of High-Dimensional Embodied Systems—0
ActSafe: Active Exploration with Safety Constraints for Reinforcement Learning—0
Robust Deep Reinforcement Learning for Volt-VAR Optimization in Active Distribution System under Uncertainty—0
Handling Long-Term Safety and Uncertainty in Safe Reinforcement LearningCode0
Revisiting Safe Exploration in Safe Reinforcement learning—0
A Safe Self-evolution Algorithm for Autonomous Driving Based on Data-Driven Risk Quantification Model—0
Meta SAC-Lag: Towards Deployable Safe Reinforcement Learning via MetaGradient-based Hyperparameter Tuning—0
A Safe Exploration Strategy for Model-free Task Adaptation in Safety-constrained Grid Environments—0
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