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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 26–50 of 135 papers

TitleStatusHype
Meta SAC-Lag: Towards Deployable Safe Reinforcement Learning via MetaGradient-based Hyperparameter Tuning—0
Data Efficient Reinforcement Learning for Legged Robots—0
Data-efficient visuomotor policy training using reinforcement learning and generative models—0
Decoupled Learning of Environment Characteristics for Safe Exploration—0
DESTA: A Framework for Safe Reinforcement Learning with Markov Games of Intervention—0
Learning to Drive Using Sparse Imitation Reinforcement Learning—0
Learning Transferable Domain Priors for Safe Exploration in Reinforcement Learning—0
ActSafe: Active Exploration with Safety Constraints for Reinforcement Learning—0
Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems—0
Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots—0
Learn-to-Race Challenge 2022: Benchmarking Safe Learning and Cross-domain Generalisation in Autonomous Racing—0
Building HVAC Scheduling Using Reinforcement Learning via Neural Network Based Model Approximation—0
Exploration of Unranked Items in Safe Online Learning to Re-Rank—0
Learning-Enhanced Safeguard Control for High-Relative-Degree Systems: Robust Optimization under Disturbances and Faults—0
Guiding Safe Exploration with Weakest Preconditions—0
A Safe Self-evolution Algorithm for Autonomous Driving Based on Data-Driven Risk Quantification Model—0
Highway Value Iteration Networks—0
A Safe Semi-supervised Graph Convolution Network—0
Information-Theoretic Safe Bayesian Optimization—0
Exploration in Deep Reinforcement Learning: A Survey—0
A safe exploration approach to constrained Markov decision processes—0
BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback—0
Approximate Shielding of Atari Agents for Safe Exploration—0
Learning-based Symbolic Abstractions for Nonlinear Control Systems—0
Learning Human-like Representations to Enable Learning Human Values—0
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