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Safe Exploration
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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
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
SLAC: Simulation-Pretrained Latent Action Space for Whole-Body Real-World RL
Bresa: Bio-inspired Reflexive Safe Reinforcement Learning for Contact-Rich Robotic Tasks
Safe exploration in reproducing kernel Hilbert spaces
Safety Representations for Safer Policy Learning
Learning to explore when mistakes are not allowed
ConRFT: A Reinforced Fine-tuning Method for VLA Models via Consistency Policy
Learning-Enhanced Safeguard Control for High-Relative-Degree Systems: Robust Optimization under Disturbances and Faults
Safe Bayesian Optimization for the Control of High-Dimensional Embodied Systems
ActSafe: Active Exploration with Safety Constraints for Reinforcement Learning
Robust Deep Reinforcement Learning for Volt-VAR Optimization in Active Distribution System under Uncertainty
Handling Long-Term Safety and Uncertainty in Safe Reinforcement Learning
Revisiting Safe Exploration in Safe Reinforcement learning
A Safe Self-evolution Algorithm for Autonomous Driving Based on Data-Driven Risk Quantification Model
Meta SAC-Lag: Towards Deployable Safe Reinforcement Learning via MetaGradient-based Hyperparameter Tuning
A Safe Exploration Strategy for Model-free Task Adaptation in Safety-constrained Grid Environments
Exterior Penalty Policy Optimization with Penalty Metric Network under Constraints
Enhanced Safety in Autonomous Driving: Integrating Latent State Diffusion Model for End-to-End Navigation
Highway Value Iteration Networks
Safe Reinforcement Learning in Black-Box Environments via Adaptive Shielding
Preparing for Black Swans: The Antifragility Imperative for Machine Learning
Contextual Affordances for Safe Exploration in Robotic Scenarios
Safe Exploration Using Bayesian World Models and Log-Barrier Optimization
Trajectory-wise Iterative Reinforcement Learning Framework for Auto-bidding
Information-Theoretic Safe Bayesian Optimization
A comparison of RL-based and PID controllers for 6-DOF swimming robots: hybrid underwater object tracking
Towards Socially and Morally Aware RL agent: Reward Design With LLM
Towards Safe Load Balancing based on Control Barrier Functions and Deep Reinforcement Learning
Learning Human-like Representations to Enable Learning Human Values
Safe Exploration in Reinforcement Learning: Training Backup Control Barrier Functions with Zero Training Time Safety Violations
A safe exploration approach to constrained Markov decision processes
Safe Reinforcement Learning in a Simulated Robotic Arm
State-Wise Safe Reinforcement Learning With Pixel Observations
Safe Exploration in Reinforcement Learning: A Generalized Formulation and Algorithms
Reinforcement Learning by Guided Safe Exploration
Probabilistic Counterexample Guidance for Safer Reinforcement Learning (Extended Version)
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