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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 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
Highway Value Iteration Networks—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
Learning Policies with Zero or Bounded Constraint Violation for Constrained MDPs—0
Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots—0
Learning to Drive Using Sparse Imitation Reinforcement Learning—0
Learning to explore when mistakes are not allowed—0
Learning Transferable Domain Priors for Safe Exploration in Reinforcement Learning—0
Learn-to-Race Challenge 2022: Benchmarking Safe Learning and Cross-domain Generalisation in Autonomous Racing—0
Linear Stochastic Bandits Under Safety Constraints—0
MESA: Offline Meta-RL for Safe Adaptation and Fault Tolerance—0
Meta SAC-Lag: Towards Deployable Safe Reinforcement Learning via MetaGradient-based Hyperparameter Tuning—0
Model-Assisted Probabilistic Safe Adaptive Control With Meta-Bayesian Learning—0
Model-Based Offline Meta-Reinforcement Learning with Regularization—0
Preparing for Black Swans: The Antifragility Imperative for Machine Learning—0
Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functions—0
Provably Efficient Primal-Dual Reinforcement Learning for CMDPs with Non-stationary Objectives and Constraints—0
Provably Efficient Safe Exploration via Primal-Dual Policy Optimization—0
Provably Learning Nash Policies in Constrained Markov Potential Games—0
Reinforcement Learning by Guided Safe Exploration—0
Revisiting Safe Exploration in Safe Reinforcement learning—0
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
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