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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 126–135 of 135 papers

TitleStatusHype
Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots—0
Learning to Drive Using Sparse Imitation Reinforcement Learning—0
Learning to explore when mistakes are not allowed—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
Meta SAC-Lag: Towards Deployable Safe Reinforcement Learning via MetaGradient-based Hyperparameter Tuning—0
Model-Assisted Probabilistic Safe Adaptive Control With Meta-Bayesian Learning—0
Model-Based Offline Meta-Reinforcement Learning with Regularization—0
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