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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 51–75 of 135 papers

TitleStatusHype
Safe model-based design of experiments using Gaussian processes—0
Safe Reinforcement Learning in a Simulated Robotic Arm—0
Safe Reinforcement Learning of Dynamic High-Dimensional Robotic Tasks: Navigation, Manipulation, Interaction—0
Safe Reinforcement Learning via Shielding under Partial Observability—0
Safe Reinforcement Learning with Contrastive Risk Prediction—0
Safe Reinforcement Learning with Dead-Ends Avoidance and Recovery—0
Safety-Guided Deep Reinforcement Learning via Online Gaussian Process Estimation—0
Safety Representations for Safer Policy Learning—0
Sample-efficient Safe Learning for Online Nonlinear Control with Control Barrier Functions—0
SCOPE: Safe Exploration for Dynamic Computer Systems Optimization—0
Safe Reinforcement Learning via Probabilistic Shields—0
SLAC: Simulation-Pretrained Latent Action Space for Whole-Body Real-World RL—0
System III: Learning with Domain Knowledge for Safety Constraints—0
Temporal Logic Guided Safe Reinforcement Learning Using Control Barrier Functions—0
Towards Safe Continuing Task Reinforcement Learning—0
Towards Safe Load Balancing based on Control Barrier Functions and Deep Reinforcement Learning—0
Towards Socially and Morally Aware RL agent: Reward Design With LLM—0
Training and Evaluation of Deep Policies using Reinforcement Learning and Generative Models—0
Trajectory-wise Iterative Reinforcement Learning Framework for Auto-bidding—0
Virtuously Safe Reinforcement Learning—0
Information-Theoretic Safe Bayesian Optimization—0
A safe exploration approach to constrained Markov decision processes—0
Learning-based Symbolic Abstractions for Nonlinear Control Systems—0
Learning-Enhanced Safeguard Control for High-Relative-Degree Systems: Robust Optimization under Disturbances and Faults—0
Learning Human-like Representations to Enable Learning Human Values—0
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