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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 Reinforcement Learning with Dead-Ends Avoidance and Recovery—0
Provably Learning Nash Policies in Constrained Markov Potential Games—0
Exploration of Unranked Items in Safe Online Learning to Re-Rank—0
System III: Learning with Domain Knowledge for Safety Constraints—0
Approximate Shielding of Atari Agents for Safe Exploration—0
Safe and Sample-efficient Reinforcement Learning for Clustered Dynamic EnvironmentsCode0
A Human-Centered Safe Robot Reinforcement Learning Framework with Interactive Behaviors—0
Information-Theoretic Safe Exploration with Gaussian ProcessesCode0
Benefits of Monotonicity in Safe Exploration with Gaussian ProcessesCode0
Atlas: Automate Online Service Configuration in Network SlicingCode0
The Pump Scheduling Problem: A Real-World Scenario for Reinforcement LearningCode0
Safe Exploration Method for Reinforcement Learning under Existence of DisturbanceCode0
Guiding Safe Exploration with Weakest Preconditions—0
Safe Reinforcement Learning of Dynamic High-Dimensional Robotic Tasks: Navigation, Manipulation, Interaction—0
Safe Reinforcement Learning with Contrastive Risk Prediction—0
Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functions—0
Sample-efficient Safe Learning for Online Nonlinear Control with Control Barrier Functions—0
Ablation Study of How Run Time Assurance Impacts the Training and Performance of Reinforcement Learning Agents—0
A Safe Semi-supervised Graph Convolution Network—0
Safe Exploration Incurs Nearly No Additional Sample Complexity for Reward-free RL—0
Effects of Safety State Augmentation on Safe ExplorationCode0
Learning to Drive Using Sparse Imitation Reinforcement Learning—0
Learn-to-Race Challenge 2022: Benchmarking Safe Learning and Cross-domain Generalisation in Autonomous Racing—0
Exploration in Deep Reinforcement Learning: A Survey—0
SCOPE: Safe Exploration for Dynamic Computer Systems Optimization—0
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