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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
Handling Long-Term Safety and Uncertainty in Safe Reinforcement LearningCode0
Revisiting Safe Exploration in Safe Reinforcement learning—0
A Safe Self-evolution Algorithm for Autonomous Driving Based on Data-Driven Risk Quantification Model—0
Meta SAC-Lag: Towards Deployable Safe Reinforcement Learning via MetaGradient-based Hyperparameter Tuning—0
A Safe Exploration Strategy for Model-free Task Adaptation in Safety-constrained Grid Environments—0
Exterior Penalty Policy Optimization with Penalty Metric Network under ConstraintsCode0
Enhanced Safety in Autonomous Driving: Integrating Latent State Diffusion Model for End-to-End Navigation—0
Highway Value Iteration Networks—0
Safe Reinforcement Learning in Black-Box Environments via Adaptive ShieldingCode0
Preparing for Black Swans: The Antifragility Imperative for Machine Learning—0
Contextual Affordances for Safe Exploration in Robotic Scenarios—0
Safe Exploration Using Bayesian World Models and Log-Barrier Optimization—0
Trajectory-wise Iterative Reinforcement Learning Framework for Auto-bidding—0
Information-Theoretic Safe Bayesian Optimization—0
A comparison of RL-based and PID controllers for 6-DOF swimming robots: hybrid underwater object trackingCode0
Towards Socially and Morally Aware RL agent: Reward Design With LLM—0
Towards Safe Load Balancing based on Control Barrier Functions and Deep Reinforcement Learning—0
Learning Human-like Representations to Enable Learning Human Values—0
Safe Exploration in Reinforcement Learning: Training Backup Control Barrier Functions with Zero Training Time Safety Violations—0
A safe exploration approach to constrained Markov decision processes—0
Safe Reinforcement Learning in a Simulated Robotic Arm—0
Safe Exploration in Reinforcement Learning: A Generalized Formulation and Algorithms—0
Reinforcement Learning by Guided Safe Exploration—0
Probabilistic Counterexample Guidance for Safer Reinforcement Learning (Extended Version)Code0
Model-Assisted Probabilistic Safe Adaptive Control With Meta-Bayesian Learning—0
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