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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
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
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