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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
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
SAAC: Safe Reinforcement Learning as an Adversarial Game of Actor-Critics—0
Training and Evaluation of Deep Policies using Reinforcement Learning and Generative Models—0
Safe Reinforcement Learning via Shielding under Partial Observability—0
Safe Exploration for Efficient Policy Evaluation and Comparison—0
CUP: A Conservative Update Policy Algorithm for Safe Reinforcement LearningCode0
Model-Based Offline Meta-Reinforcement Learning with Regularization—0
Provably Efficient Primal-Dual Reinforcement Learning for CMDPs with Non-stationary Objectives and Constraints—0
GoSafeOpt: Scalable Safe Exploration for Global Optimization of Dynamical SystemsCode0
MESA: Offline Meta-RL for Safe Adaptation and Fault Tolerance—0
DOPE: Doubly Optimistic and Pessimistic Exploration for Safe Reinforcement LearningCode0
Safe Policy Optimization with Local Generalized Linear Function ApproximationsCode0
Infinite Time Horizon Safety of Bayesian Neural NetworksCode0
DESTA: A Framework for Safe Reinforcement Learning with Markov Games of Intervention—0
Dual-Arm Adversarial Robot Learning—0
Safe Exploration in Linear Equality Constraint—0
Safety-Critical Learning of Robot Control with Temporal Logic Specifications—0
Bayesian Controller Fusion: Leveraging Control Priors in Deep Reinforcement Learning for Robotics—0
Safe Exploration by Solving Early Terminated MDP—0
Learning Policies with Zero or Bounded Constraint Violation for Constrained MDPs—0
Safe Exploration in Model-based Reinforcement Learning using Control Barrier Functions—0
Safe Continuous Control with Constrained Model-Based Policy OptimizationCode0
Towards Safe Continuing Task Reinforcement Learning—0
Safe model-based design of experiments using Gaussian processes—0
Conservative Safety Critics for Exploration—0
Data-efficient visuomotor policy training using reinforcement learning and generative models—0
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