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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 101–110 of 135 papers

TitleStatusHype
A Safe Semi-supervised Graph Convolution Network—0
Avoiding Negative Side-Effects and Promoting Safe Exploration with Imaginative Planning—0
Bayesian Controller Fusion: Leveraging Control Priors in Deep Reinforcement Learning for Robotics—0
Bresa: Bio-inspired Reflexive Safe Reinforcement Learning for Contact-Rich Robotic Tasks—0
BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback—0
Building HVAC Scheduling Using Reinforcement Learning via Neural Network Based Model Approximation—0
Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems—0
Conservative Safety Critics for Exploration—0
Contextual Affordances for Safe Exploration in Robotic Scenarios—0
Data Efficient Reinforcement Learning for Legged Robots—0
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