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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
Trajectory-wise Iterative Reinforcement Learning Framework for Auto-bidding—0
A Safe Exploration Strategy for Model-free Task Adaptation in Safety-constrained Grid Environments—0
Virtuously Safe Reinforcement Learning—0
A Bayesian Approach to Robust Reinforcement Learning—0
Ablation Study of How Run Time Assurance Impacts the Training and Performance of Reinforcement Learning Agents—0
ActSafe: Active Exploration with Safety Constraints for Reinforcement Learning—0
A Human-Centered Safe Robot Reinforcement Learning Framework with Interactive Behaviors—0
Approximate Shielding of Atari Agents for Safe Exploration—0
A Safe Self-evolution Algorithm for Autonomous Driving Based on Data-Driven Risk Quantification Model—0
A Safe Semi-supervised Graph Convolution Network—0
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