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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 21–30 of 135 papers

Title	Date	Tasks	Status	Hype	Score
DOPE: Doubly Optimistic and Pessimistic Exploration for Safe Reinforcement Learning	Dec 1, 2021	reinforcement-learningReinforcement Learning	CodeCode Available	0	5
Confidence-Guided Human-AI Collaboration: Reinforcement Learning with Distributional Proxy Value Propagation for Autonomous Driving	Jun 4, 2025	Autonomous DrivingImitation Learning	CodeCode Available	0	5
Information-Theoretic Safe Exploration with Gaussian Processes	Dec 9, 2022	Decision MakingGaussian Processes	CodeCode Available	0	5
Concrete Problems in AI Safety	Jun 21, 2016	BIG-bench Machine LearningSafe Exploration	CodeCode Available	0	5
Handling Long-Term Safety and Uncertainty in Safe Reinforcement Learning	Sep 18, 2024	reinforcement-learningReinforcement Learning	CodeCode Available	0	5
Curiosity Killed or Incapacitated the Cat and the Asymptotically Optimal Agent	Jun 5, 2020	reinforcement-learningReinforcement Learning	CodeCode Available	0	5
AI Safety Gridworlds	Nov 27, 2017	Deep Reinforcement Learningreinforcement-learning	CodeCode Available	0	5
Infinite Time Horizon Safety of Bayesian Neural Networks	Nov 4, 2021	reinforcement-learningReinforcement Learning	CodeCode Available	0	5
Learning-based Model Predictive Control for Safe Exploration	Mar 22, 2018	modelModel Predictive Control	CodeCode Available	0	5
Effects of Safety State Augmentation on Safe Exploration	Jun 6, 2022	Reinforcement Learning (RL)Safe Exploration	CodeCode Available	0	5

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