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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–60 of 135 papers

TitleStatusHype
Near-Optimal Multi-Agent Learning for Safe Coverage ControlCode1
Safe Exploration Method for Reinforcement Learning under Existence of DisturbanceCode0
Guiding Safe Exploration with Weakest Preconditions—0
Safe Reinforcement Learning of Dynamic High-Dimensional Robotic Tasks: Navigation, Manipulation, Interaction—0
Toward Safe and Accelerated Deep Reinforcement Learning for Next-Generation Wireless NetworksCode1
Safe Reinforcement Learning with Contrastive Risk Prediction—0
Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functions—0
Sample-efficient Safe Learning for Online Nonlinear Control with Control Barrier Functions—0
Ablation Study of How Run Time Assurance Impacts the Training and Performance of Reinforcement Learning Agents—0
A Safe Semi-supervised Graph Convolution Network—0
Show:102550
← PrevPage 6 of 14Next →

No leaderboard results yet.