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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 61–70 of 135 papers

TitleStatusHype
The Pump Scheduling Problem: A Real-World Scenario for Reinforcement LearningCode0
Safe Exploration Method for Reinforcement Learning under Existence of DisturbanceCode0
Guiding Safe Exploration with Weakest Preconditions—0
Safe Reinforcement Learning of Dynamic High-Dimensional Robotic Tasks: Navigation, Manipulation, Interaction—0
Safe Reinforcement Learning with Contrastive Risk Prediction—0
Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functions—0
Sample-efficient Safe Learning for Online Nonlinear Control with Control Barrier Functions—0
Ablation Study of How Run Time Assurance Impacts the Training and Performance of Reinforcement Learning Agents—0
A Safe Semi-supervised Graph Convolution Network—0
Safe Exploration Incurs Nearly No Additional Sample Complexity for Reward-free RL—0
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