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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 11–20 of 135 papers

TitleStatusHype
Neurosymbolic Reinforcement Learning with Formally Verified ExplorationCode1
Provably Safe PAC-MDP Exploration Using AnalogiesCode1
Verifiably Safe Exploration for End-to-End Reinforcement LearningCode1
SafeML: Safety Monitoring of Machine Learning Classifiers through Statistical Difference MeasureCode1
Safe Exploration in Continuous Action SpacesCode1
SLAC: Simulation-Pretrained Latent Action Space for Whole-Body Real-World RL—0
Confidence-Guided Human-AI Collaboration: Reinforcement Learning with Distributional Proxy Value Propagation for Autonomous DrivingCode0
Bresa: Bio-inspired Reflexive Safe Reinforcement Learning for Contact-Rich Robotic Tasks—0
Safe exploration in reproducing kernel Hilbert spaces—0
Safety Representations for Safer Policy Learning—0
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