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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 126–135 of 135 papers

Title	Date	Tasks	Status	Hype
Safe reinforcement learning for probabilistic reachability and safety specifications: A Lyapunov-based approach	Feb 24, 2020	Autonomous Drivingcontinuous-control	CodeCode Available	0
Exterior Penalty Policy Optimization with Penalty Metric Network under Constraints	Jul 22, 2024	Safe Exploration	CodeCode Available	0
Benefits of Monotonicity in Safe Exploration with Gaussian Processes	Nov 3, 2022	Gaussian ProcessesSafe Exploration	CodeCode Available	0
Safe Reinforcement Learning in Black-Box Environments via Adaptive Shielding	May 28, 2024	reinforcement-learningReinforcement Learning (RL)	CodeCode Available	0
DOPE: Doubly Optimistic and Pessimistic Exploration for Safe Reinforcement Learning	Dec 1, 2021	reinforcement-learningReinforcement Learning	CodeCode Available	0
Effects of Safety State Augmentation on Safe Exploration	Jun 6, 2022	Reinforcement Learning (RL)Safe Exploration	CodeCode Available	0
Enforcing Almost-Sure Reachability in POMDPs	Jun 30, 2020	Decision Makingreinforcement-learning	CodeCode Available	0
Curiosity Killed or Incapacitated the Cat and the Asymptotically Optimal Agent	Jun 5, 2020	reinforcement-learningReinforcement Learning	CodeCode Available	0
Safe Exploration Method for Reinforcement Learning under Existence of Disturbance	Sep 30, 2022	reinforcement-learningReinforcement Learning	CodeCode Available	0
Probabilistic Counterexample Guidance for Safer Reinforcement Learning (Extended Version)	Jul 10, 2023	reinforcement-learningReinforcement Learning	CodeCode Available	0

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