SOTAVerified

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 111–120 of 135 papers

TitleStatusHype
Data-efficient visuomotor policy training using reinforcement learning and generative models—0
Decoupled Learning of Environment Characteristics for Safe Exploration—0
DESTA: A Framework for Safe Reinforcement Learning with Markov Games of Intervention—0
Dual-Arm Adversarial Robot Learning—0
Efficient and Safe Exploration in Deterministic Markov Decision Processes with Unknown Transition Models—0
Enhanced Safety in Autonomous Driving: Integrating Latent State Diffusion Model for End-to-End Navigation—0
Effects of Safety State Augmentation on Safe Exploration—0
Exploration in Deep Reinforcement Learning: A Survey—0
Exploration of Unranked Items in Safe Online Learning to Re-Rank—0
Guiding Safe Exploration with Weakest Preconditions—0
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