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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 101–135 of 135 papers

TitleStatusHype
Avoiding Negative Side-Effects and Promoting Safe Exploration with Imaginative Planning—0
Bayesian Controller Fusion: Leveraging Control Priors in Deep Reinforcement Learning for Robotics—0
Bresa: Bio-inspired Reflexive Safe Reinforcement Learning for Contact-Rich Robotic Tasks—0
BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback—0
Building HVAC Scheduling Using Reinforcement Learning via Neural Network Based Model Approximation—0
Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems—0
Conservative Safety Critics for Exploration—0
Contextual Affordances for Safe Exploration in Robotic Scenarios—0
Data Efficient Reinforcement Learning for Legged Robots—0
Data-efficient visuomotor policy training using reinforcement learning and generative models—0
Decoupled Learning of Environment Characteristics for Safe Exploration—0
DESTA: A Framework for Safe Reinforcement Learning with Markov Games of Intervention—0
Dual-Arm Adversarial Robot Learning—0
Efficient and Safe Exploration in Deterministic Markov Decision Processes with Unknown Transition Models—0
Enhanced Safety in Autonomous Driving: Integrating Latent State Diffusion Model for End-to-End Navigation—0
Exploration in Deep Reinforcement Learning: A Survey—0
Exploration of Unranked Items in Safe Online Learning to Re-Rank—0
Guiding Safe Exploration with Weakest Preconditions—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
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