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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
Confidence-Guided Human-AI Collaboration: Reinforcement Learning with Distributional Proxy Value Propagation for Autonomous DrivingCode0
SLAC: Simulation-Pretrained Latent Action Space for Whole-Body Real-World RL—0
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
ConRFT: A Reinforced Fine-tuning Method for VLA Models via Consistency PolicyCode3
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
Handling Long-Term Safety and Uncertainty in Safe Reinforcement LearningCode0
Revisiting Safe Exploration in Safe Reinforcement learning—0
A Safe Self-evolution Algorithm for Autonomous Driving Based on Data-Driven Risk Quantification Model—0
Meta SAC-Lag: Towards Deployable Safe Reinforcement Learning via MetaGradient-based Hyperparameter Tuning—0
A Safe Exploration Strategy for Model-free Task Adaptation in Safety-constrained Grid Environments—0
Exterior Penalty Policy Optimization with Penalty Metric Network under ConstraintsCode0
Transductive Active Learning with Application to Safe Bayesian OptimizationCode1
Enhanced Safety in Autonomous Driving: Integrating Latent State Diffusion Model for End-to-End Navigation—0
Highway Value Iteration Networks—0
Safe Reinforcement Learning in Black-Box Environments via Adaptive ShieldingCode0
Preparing for Black Swans: The Antifragility Imperative for Machine Learning—0
Contextual Affordances for Safe Exploration in Robotic Scenarios—0
Safe Exploration Using Bayesian World Models and Log-Barrier Optimization—0
Information-Theoretic Safe Bayesian Optimization—0
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