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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 51–75 of 135 papers

TitleStatusHype
Robust Deep Reinforcement Learning for Volt-VAR Optimization in Active Distribution System under Uncertainty—0
Robust Regression for Safe Exploration in Control—0
SAAC: Safe Reinforcement Learning as an Adversarial Game of Actor-Critics—0
Safe Bayesian Optimization for the Control of High-Dimensional Embodied Systems—0
Safety-Critical Learning of Robot Control with Temporal Logic Specifications—0
Safe deep reinforcement learning-based constrained optimal control scheme for active distribution networks—0
Safe Exploration by Solving Early Terminated MDP—0
Safe Exploration for Efficient Policy Evaluation and Comparison—0
Safe Exploration for Identifying Linear Systems via Robust Optimization—0
Safe Exploration for Interactive Machine Learning—0
Safe Exploration Incurs Nearly No Additional Sample Complexity for Reward-free RL—0
Safe Exploration in Linear Equality Constraint—0
Safe Exploration in Markov Decision Processes with Time-Variant Safety using Spatio-Temporal Gaussian Process—0
Safe Exploration in Markov Decision Processes—0
Safe Exploration in Model-based Reinforcement Learning using Control Barrier Functions—0
Safe Exploration in Reinforcement Learning: Training Backup Control Barrier Functions with Zero Training Time Safety Violations—0
Safe Exploration in Reinforcement Learning: A Generalized Formulation and Algorithms—0
Safe exploration in reproducing kernel Hilbert spaces—0
A predictive safety filter for learning-based control of constrained nonlinear dynamical systems—0
Safe Exploration of State and Action Spaces in Reinforcement Learning—0
Safe Exploration Using Bayesian World Models and Log-Barrier Optimization—0
Safe Interactive Model-Based Learning—0
Safe model-based design of experiments using Gaussian processes—0
Safe Reinforcement Learning in a Simulated Robotic Arm—0
Safe Reinforcement Learning of Dynamic High-Dimensional Robotic Tasks: Navigation, Manipulation, Interaction—0
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