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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 1–25 of 135 papers

TitleStatusHype
ConRFT: A Reinforced Fine-tuning Method for VLA Models via Consistency PolicyCode3
MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement LearningCode2
Transductive Active Learning with Application to Safe Bayesian OptimizationCode1
State-Wise Safe Reinforcement Learning With Pixel ObservationsCode1
Model-based Safe Deep Reinforcement Learning via a Constrained Proximal Policy Optimization AlgorithmCode1
Near-Optimal Multi-Agent Learning for Safe Coverage ControlCode1
Toward Safe and Accelerated Deep Reinforcement Learning for Next-Generation Wireless NetworksCode1
Feasible Actor-Critic: Constrained Reinforcement Learning for Ensuring Statewise SafetyCode1
Autonomous UAV Exploration of Dynamic Environments via Incremental Sampling and Probabilistic RoadmapCode1
Align-RUDDER: Learning From Few Demonstrations by Reward RedistributionCode1
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
Learning to explore when mistakes are not allowed—0
Learning-Enhanced Safeguard Control for High-Relative-Degree Systems: Robust Optimization under Disturbances and Faults—0
Safe Bayesian Optimization for the Control of High-Dimensional Embodied Systems—0
ActSafe: Active Exploration with Safety Constraints for Reinforcement Learning—0
Robust Deep Reinforcement Learning for Volt-VAR Optimization in Active Distribution System under Uncertainty—0
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