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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 71–80 of 135 papers

TitleStatusHype
Information-Theoretic Safe Bayesian Optimization—0
A safe exploration approach to constrained Markov decision processes—0
Learning-based Symbolic Abstractions for Nonlinear Control Systems—0
Learning-Enhanced Safeguard Control for High-Relative-Degree Systems: Robust Optimization under Disturbances and Faults—0
Learning Human-like Representations to Enable Learning Human Values—0
Learning Policies with Zero or Bounded Constraint Violation for Constrained MDPs—0
Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots—0
Learning to Drive Using Sparse Imitation Reinforcement Learning—0
Learning to explore when mistakes are not allowed—0
Learning Transferable Domain Priors for Safe Exploration in Reinforcement Learning—0
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