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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 1–10 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
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