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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 91–100 of 135 papers

TitleStatusHype
Provably Learning Nash Policies in Constrained Markov Potential Games—0
Reinforcement Learning by Guided Safe Exploration—0
Revisiting Safe Exploration in Safe Reinforcement learning—0
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
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