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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 81–90 of 135 papers

TitleStatusHype
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
Preparing for Black Swans: The Antifragility Imperative for Machine Learning—0
Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functions—0
Provably Efficient Primal-Dual Reinforcement Learning for CMDPs with Non-stationary Objectives and Constraints—0
Provably Efficient Safe Exploration via Primal-Dual Policy Optimization—0
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