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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 to Drive Using Sparse Imitation Reinforcement Learning—0
Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots—0
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 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
Learning-based Symbolic Abstractions for Nonlinear Control Systems—0
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