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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

Showing 101–125 of 135 papers

TitleStatusHype
Safe Exploration for Efficient Policy Evaluation and Comparison—0
Safe Exploration for Identifying Linear Systems via Robust Optimization—0
Safe Exploration for Interactive Machine Learning—0
Safe Exploration Incurs Nearly No Additional Sample Complexity for Reward-free RL—0
Safe Exploration in Linear Equality Constraint—0
Safe Exploration in Markov Decision Processes with Time-Variant Safety using Spatio-Temporal Gaussian Process—0
Safe Exploration in Markov Decision Processes—0
Safe Exploration in Model-based Reinforcement Learning using Control Barrier Functions—0
Infinite Time Horizon Safety of Bayesian Neural NetworksCode0
GoSafeOpt: Scalable Safe Exploration for Global Optimization of Dynamical SystemsCode0
Information-Theoretic Safe Exploration with Gaussian ProcessesCode0
Safe Exploration for Optimizing Contextual BanditsCode0
Learning-based Model Predictive Control for Safe ExplorationCode0
Learning-based Model Predictive Control for Safe Exploration and Reinforcement LearningCode0
Confidence-Guided Human-AI Collaboration: Reinforcement Learning with Distributional Proxy Value Propagation for Autonomous DrivingCode0
CUP: A Conservative Update Policy Algorithm for Safe Reinforcement LearningCode0
AI Safety GridworldsCode0
Safe Exploration in Finite Markov Decision Processes with Gaussian ProcessesCode0
The Pump Scheduling Problem: A Real-World Scenario for Reinforcement LearningCode0
Concrete Problems in AI SafetyCode0
Atlas: Automate Online Service Configuration in Network SlicingCode0
Safe and Sample-efficient Reinforcement Learning for Clustered Dynamic EnvironmentsCode0
Safe Policy Optimization with Local Generalized Linear Function ApproximationsCode0
Safe Continuous Control with Constrained Model-Based Policy OptimizationCode0
Safe reinforcement learning for probabilistic reachability and safety specifications: A Lyapunov-based approachCode0
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