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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 150 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
Safe Exploration in Continuous Action SpacesCode1
Provably Safe PAC-MDP Exploration Using AnalogiesCode1
Model-based Safe Deep Reinforcement Learning via a Constrained Proximal Policy Optimization AlgorithmCode1
State-Wise Safe Reinforcement Learning With Pixel ObservationsCode1
Transductive Active Learning with Application to Safe Bayesian OptimizationCode1
SafeML: Safety Monitoring of Machine Learning Classifiers through Statistical Difference MeasureCode1
Toward Safe and Accelerated Deep Reinforcement Learning for Next-Generation Wireless NetworksCode1
Align-RUDDER: Learning From Few Demonstrations by Reward RedistributionCode1
Verifiably Safe Exploration for End-to-End Reinforcement LearningCode1
Neurosymbolic Reinforcement Learning with Formally Verified ExplorationCode1
Autonomous UAV Exploration of Dynamic Environments via Incremental Sampling and Probabilistic RoadmapCode1
Feasible Actor-Critic: Constrained Reinforcement Learning for Ensuring Statewise SafetyCode1
Near-Optimal Multi-Agent Learning for Safe Coverage ControlCode1
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
Meta SAC-Lag: Towards Deployable Safe Reinforcement Learning via MetaGradient-based Hyperparameter Tuning0
MESA: Offline Meta-RL for Safe Adaptation and Fault Tolerance0
Model-Assisted Probabilistic Safe Adaptive Control With Meta-Bayesian Learning0
Model-Based Offline Meta-Reinforcement Learning with Regularization0
Linear Stochastic Bandits Under Safety Constraints0
Contextual Affordances for Safe Exploration in Robotic Scenarios0
Learning to Drive Using Sparse Imitation Reinforcement Learning0
Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots0
Data Efficient Reinforcement Learning for Legged Robots0
Data-efficient visuomotor policy training using reinforcement learning and generative models0
Decoupled Learning of Environment Characteristics for Safe Exploration0
DESTA: A Framework for Safe Reinforcement Learning with Markov Games of Intervention0
Learning to explore when mistakes are not allowed0
ActSafe: Active Exploration with Safety Constraints for Reinforcement Learning0
Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems0
Learning Policies with Zero or Bounded Constraint Violation for Constrained MDPs0
Learning Transferable Domain Priors for Safe Exploration in Reinforcement Learning0
Building HVAC Scheduling Using Reinforcement Learning via Neural Network Based Model Approximation0
Exploration of Unranked Items in Safe Online Learning to Re-Rank0
Approximate Shielding of Atari Agents for Safe Exploration0
Exploration in Deep Reinforcement Learning: A Survey0
Guiding Safe Exploration with Weakest Preconditions0
A Safe Self-evolution Algorithm for Autonomous Driving Based on Data-Driven Risk Quantification Model0
Highway Value Iteration Networks0
A Safe Semi-supervised Graph Convolution Network0
Information-Theoretic Safe Bayesian Optimization0
Effects of Safety State Augmentation on Safe Exploration0
A safe exploration approach to constrained Markov decision processes0
Conservative Safety Critics for Exploration0
A Human-Centered Safe Robot Reinforcement Learning Framework with Interactive Behaviors0
Learning-based Symbolic Abstractions for Nonlinear Control Systems0
BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback0
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