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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 31–40 of 135 papers

TitleStatusHype
A safe exploration approach to constrained Markov decision processes—0
A Safe Self-evolution Algorithm for Autonomous Driving Based on Data-Driven Risk Quantification Model—0
Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems—0
ActSafe: Active Exploration with Safety Constraints for Reinforcement Learning—0
Learning-based Symbolic Abstractions for Nonlinear Control Systems—0
Building HVAC Scheduling Using Reinforcement Learning via Neural Network Based Model Approximation—0
Exploration of Unranked Items in Safe Online Learning to Re-Rank—0
Guiding Safe Exploration with Weakest Preconditions—0
Exploration in Deep Reinforcement Learning: A Survey—0
BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback—0
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