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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 61–70 of 135 papers

TitleStatusHype
Safe Exploration Incurs Nearly No Additional Sample Complexity for Reward-free RL—0
Safe Exploration in Linear Equality Constraint—0
Safe Exploration in Markov Decision Processes with Time-Variant Safety using Spatio-Temporal Gaussian Process—0
Safe Exploration in Markov Decision Processes—0
Safe Exploration in Model-based Reinforcement Learning using Control Barrier Functions—0
Safe Exploration in Reinforcement Learning: Training Backup Control Barrier Functions with Zero Training Time Safety Violations—0
Safe Exploration in Reinforcement Learning: A Generalized Formulation and Algorithms—0
Safe exploration in reproducing kernel Hilbert spaces—0
A predictive safety filter for learning-based control of constrained nonlinear dynamical systems—0
Safe Exploration of State and Action Spaces in Reinforcement Learning—0
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