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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 51–100 of 135 papers

TitleStatusHype
Robust Deep Reinforcement Learning for Volt-VAR Optimization in Active Distribution System under Uncertainty—0
Robust Regression for Safe Exploration in Control—0
SAAC: Safe Reinforcement Learning as an Adversarial Game of Actor-Critics—0
Safe Bayesian Optimization for the Control of High-Dimensional Embodied Systems—0
Safety-Critical Learning of Robot Control with Temporal Logic Specifications—0
Safe deep reinforcement learning-based constrained optimal control scheme for active distribution networks—0
Safe Exploration by Solving Early Terminated MDP—0
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
Safe Exploration in Reinforcement Learning: Training Backup Control Barrier Functions with Zero Training Time Safety Violations—0
Safe Exploration in Reinforcement Learning: A Generalized Formulation and Algorithms—0
Safe exploration in reproducing kernel Hilbert spaces—0
A predictive safety filter for learning-based control of constrained nonlinear dynamical systems—0
Safe Exploration of State and Action Spaces in Reinforcement Learning—0
Safe Exploration Using Bayesian World Models and Log-Barrier Optimization—0
Safe Interactive Model-Based Learning—0
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
A Safe Exploration Strategy for Model-free Task Adaptation in Safety-constrained Grid Environments—0
Virtuously Safe Reinforcement Learning—0
A Bayesian Approach to Robust Reinforcement Learning—0
Ablation Study of How Run Time Assurance Impacts the Training and Performance of Reinforcement Learning Agents—0
ActSafe: Active Exploration with Safety Constraints for Reinforcement Learning—0
A Human-Centered Safe Robot Reinforcement Learning Framework with Interactive Behaviors—0
Approximate Shielding of Atari Agents for Safe Exploration—0
A Safe Self-evolution Algorithm for Autonomous Driving Based on Data-Driven Risk Quantification Model—0
A Safe Semi-supervised Graph Convolution Network—0
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