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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 11–20 of 135 papers

Title	Date	Tasks	Status	Hype	Score
Transductive Active Learning with Application to Safe Bayesian Optimization	Jul 12, 2024	Active LearningBayesian Optimization	CodeCode Available	1	5
Autonomous UAV Exploration of Dynamic Environments via Incremental Sampling and Probabilistic Roadmap	Oct 14, 2020	Safe Exploration	CodeCode Available	1	5
Neurosymbolic Reinforcement Learning with Formally Verified Exploration	Sep 26, 2020	reinforcement-learningReinforcement Learning	CodeCode Available	1	5
Model-based Safe Deep Reinforcement Learning via a Constrained Proximal Policy Optimization Algorithm	Oct 14, 2022	Deep Reinforcement Learningreinforcement-learning	CodeCode Available	1	5
Toward Safe and Accelerated Deep Reinforcement Learning for Next-Generation Wireless Networks	Sep 16, 2022	Deep Reinforcement LearningManagement	CodeCode Available	1	5
Infinite Time Horizon Safety of Bayesian Neural Networks	Nov 4, 2021	reinforcement-learningReinforcement Learning	CodeCode Available	0	5
AI Safety Gridworlds	Nov 27, 2017	Deep Reinforcement Learningreinforcement-learning	CodeCode Available	0	5
Information-Theoretic Safe Exploration with Gaussian Processes	Dec 9, 2022	Decision MakingGaussian Processes	CodeCode Available	0	5
Atlas: Automate Online Service Configuration in Network Slicing	Oct 30, 2022	Bayesian OptimizationSafe 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

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