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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

Showing 125 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
Provably Safe PAC-MDP Exploration Using AnalogiesCode1
Neurosymbolic Reinforcement Learning with Formally Verified ExplorationCode1
State-Wise Safe Reinforcement Learning With Pixel ObservationsCode1
SafeML: Safety Monitoring of Machine Learning Classifiers through Statistical Difference MeasureCode1
Toward Safe and Accelerated Deep Reinforcement Learning for Next-Generation Wireless NetworksCode1
Safe Exploration in Continuous Action SpacesCode1
Feasible Actor-Critic: Constrained Reinforcement Learning for Ensuring Statewise SafetyCode1
Transductive Active Learning with Application to Safe Bayesian OptimizationCode1
Verifiably Safe Exploration for End-to-End Reinforcement LearningCode1
Autonomous UAV Exploration of Dynamic Environments via Incremental Sampling and Probabilistic RoadmapCode1
Near-Optimal Multi-Agent Learning for Safe Coverage ControlCode1
Model-based Safe Deep Reinforcement Learning via a Constrained Proximal Policy Optimization AlgorithmCode1
Align-RUDDER: Learning From Few Demonstrations by Reward RedistributionCode1
AI Safety GridworldsCode0
Learning-based Model Predictive Control for Safe ExplorationCode0
Atlas: Automate Online Service Configuration in Network SlicingCode0
Learning-based Model Predictive Control for Safe Exploration and Reinforcement LearningCode0
Confidence-Guided Human-AI Collaboration: Reinforcement Learning with Distributional Proxy Value Propagation for Autonomous DrivingCode0
Handling Long-Term Safety and Uncertainty in Safe Reinforcement LearningCode0
Concrete Problems in AI SafetyCode0
Exterior Penalty Policy Optimization with Penalty Metric Network under ConstraintsCode0
Infinite Time Horizon Safety of Bayesian Neural NetworksCode0
Curiosity Killed or Incapacitated the Cat and the Asymptotically Optimal AgentCode0
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