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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
Safe Exploration in Reinforcement Learning: Training Backup Control Barrier Functions with Zero Training Time Safety Violations—0
A safe exploration approach to constrained Markov decision processes—0
Safe Reinforcement Learning in a Simulated Robotic Arm—0
State-Wise Safe Reinforcement Learning With Pixel ObservationsCode1
Safe Exploration in Reinforcement Learning: A Generalized Formulation and Algorithms—0
Reinforcement Learning by Guided Safe Exploration—0
Probabilistic Counterexample Guidance for Safer Reinforcement Learning (Extended Version)Code0
Model-Assisted Probabilistic Safe Adaptive Control With Meta-Bayesian Learning—0
Safe Reinforcement Learning with Dead-Ends Avoidance and Recovery—0
Provably Learning Nash Policies in Constrained Markov Potential Games—0
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