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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
SafeML: Safety Monitoring of Machine Learning Classifiers through Statistical Difference MeasureCode1
Verifiably Safe Exploration for End-to-End Reinforcement LearningCode1
Toward Safe and Accelerated Deep Reinforcement Learning for Next-Generation Wireless NetworksCode1
Model-based Safe Deep Reinforcement Learning via a Constrained Proximal Policy Optimization AlgorithmCode1
Neurosymbolic Reinforcement Learning with Formally Verified ExplorationCode1
Feasible Actor-Critic: Constrained Reinforcement Learning for Ensuring Statewise SafetyCode1
State-Wise Safe Reinforcement Learning With Pixel ObservationsCode1
Transductive Active Learning with Application to Safe Bayesian OptimizationCode1
Align-RUDDER: Learning From Few Demonstrations by Reward RedistributionCode1
Autonomous UAV Exploration of Dynamic Environments via Incremental Sampling and Probabilistic RoadmapCode1
Safe Exploration in Continuous Action SpacesCode1
Near-Optimal Multi-Agent Learning for Safe Coverage ControlCode1
Provably Safe PAC-MDP Exploration Using AnalogiesCode1
Bayesian Controller Fusion: Leveraging Control Priors in Deep Reinforcement Learning for Robotics0
Avoiding Negative Side-Effects and Promoting Safe Exploration with Imaginative Planning0
Ablation Study of How Run Time Assurance Impacts the Training and Performance of Reinforcement Learning Agents0
A Human-Centered Safe Robot Reinforcement Learning Framework with Interactive Behaviors0
DESTA: A Framework for Safe Reinforcement Learning with Markov Games of Intervention0
Dual-Arm Adversarial Robot Learning0
A Safe Semi-supervised Graph Convolution Network0
Conservative Safety Critics for Exploration0
Contextual Affordances for Safe Exploration in Robotic Scenarios0
Data Efficient Reinforcement Learning for Legged Robots0
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