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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 51–60 of 135 papers

TitleStatusHype
Learning Policies with Zero or Bounded Constraint Violation for Constrained MDPs—0
Learning Human-like Representations to Enable Learning Human Values—0
Learning-Enhanced Safeguard Control for High-Relative-Degree Systems: Robust Optimization under Disturbances and Faults—0
Learning to Drive Using Sparse Imitation Reinforcement Learning—0
Learning-based Symbolic Abstractions for Nonlinear Control Systems—0
Learning Transferable Domain Priors for Safe Exploration in Reinforcement Learning—0
Learn-to-Race Challenge 2022: Benchmarking Safe Learning and Cross-domain Generalisation in Autonomous Racing—0
Linear Stochastic Bandits Under Safety Constraints—0
MESA: Offline Meta-RL for Safe Adaptation and Fault Tolerance—0
Contextual Affordances for Safe Exploration in Robotic Scenarios—0
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