Safe Exploration Using Bayesian World Models and Log-Barrier Optimization
Fuente:
arXiv
Saved in:
| Main Authors: | As, Yarden, Sukhija, Bhavya, Krause, Andreas |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Active Few-Shot Fine-Tuning
by: Hübotter, Jonas, et al.
Published: (2024)
by: Hübotter, Jonas, et al.
Published: (2024)
Transductive Active Learning: Theory and Applications
by: Hübotter, Jonas, et al.
Published: (2024)
by: Hübotter, Jonas, et al.
Published: (2024)
Sample-efficient and Scalable Exploration in Continuous-Time RL
by: Iten, Klemens, et al.
Published: (2025)
by: Iten, Klemens, et al.
Published: (2025)
ActSafe: Active Exploration with Safety Constraints for Reinforcement Learning
by: As, Yarden, et al.
Published: (2024)
by: As, Yarden, et al.
Published: (2024)
Safe Exploration via Policy Priors
by: Wendl, Manuel, et al.
Published: (2026)
by: Wendl, Manuel, et al.
Published: (2026)
MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization
by: Sukhija, Bhavya, et al.
Published: (2024)
by: Sukhija, Bhavya, et al.
Published: (2024)
Simulation Priors for Data-Efficient Deep Learning
by: Treven, Lenart, et al.
Published: (2025)
by: Treven, Lenart, et al.
Published: (2025)
When to Sense and Control? A Time-adaptive Approach for Continuous-Time RL
by: Treven, Lenart, et al.
Published: (2024)
by: Treven, Lenart, et al.
Published: (2024)
Learning Safety Constraints for Large Language Models
by: Chen, Xin, et al.
Published: (2025)
by: Chen, Xin, et al.
Published: (2025)
How Log-Barrier Helps Exploration in Policy Optimization
by: Cesani, Leonardo, et al.
Published: (2026)
by: Cesani, Leonardo, et al.
Published: (2026)
Sampling-Based Safe Reinforcement Learning
by: Vignola, Luca, et al.
Published: (2026)
by: Vignola, Luca, et al.
Published: (2026)
NeoRL: Efficient Exploration for Nonepisodic RL
by: Sukhija, Bhavya, et al.
Published: (2024)
by: Sukhija, Bhavya, et al.
Published: (2024)
Data-Efficient Task Generalization via Probabilistic Model-based Meta Reinforcement Learning
by: Bhardwaj, Arjun, et al.
Published: (2023)
by: Bhardwaj, Arjun, et al.
Published: (2023)
Symmetry-Guided Memory Augmentation for Efficient Locomotion Learning
by: Bao, Kaixi, et al.
Published: (2025)
by: Bao, Kaixi, et al.
Published: (2025)
Learning Soft Robotic Dynamics with Active Exploration
by: Zheng, Hehui, et al.
Published: (2025)
by: Zheng, Hehui, et al.
Published: (2025)
Robotic World Model: A Neural Network Simulator for Robust Policy Optimization in Robotics
by: Li, Chenhao, et al.
Published: (2025)
by: Li, Chenhao, et al.
Published: (2025)
Bridging the Sim-to-Real Gap with Bayesian Inference
by: Rothfuss, Jonas, et al.
Published: (2024)
by: Rothfuss, Jonas, et al.
Published: (2024)
Information-Theoretic Safe Bayesian Optimization
by: Bottero, Alessandro G., et al.
Published: (2024)
by: Bottero, Alessandro G., et al.
Published: (2024)
Uncertainty-Aware Robotic World Model Makes Offline Model-Based Reinforcement Learning Work on Real Robots
by: Li, Chenhao, et al.
Published: (2025)
by: Li, Chenhao, et al.
Published: (2025)
SOMBRL: Scalable and Optimistic Model-Based RL
by: Sukhija, Bhavya, et al.
Published: (2025)
by: Sukhija, Bhavya, et al.
Published: (2025)
Revisiting Safe Exploration in Safe Reinforcement learning
by: Eckel, David, et al.
Published: (2024)
by: Eckel, David, et al.
Published: (2024)
POETS: Uncertainty-Aware LLM Optimization via Compute-Efficient Policy Ensembles
by: Menet, Nicolas, et al.
Published: (2026)
by: Menet, Nicolas, et al.
Published: (2026)
Reinforcement Learning by Guided Safe Exploration
by: Yang, Qisong, et al.
Published: (2023)
by: Yang, Qisong, et al.
Published: (2023)
Model-Based Reinforcement Learning for Control under Time-Varying Dynamics
by: Iten, Klemens, et al.
Published: (2026)
by: Iten, Klemens, et al.
Published: (2026)
Probabilistic Artificial Intelligence
by: Krause, Andreas, et al.
Published: (2025)
by: Krause, Andreas, et al.
Published: (2025)
Deep SPI: Safe Policy Improvement via World Models
by: Delgrange, Florent, et al.
Published: (2025)
by: Delgrange, Florent, et al.
Published: (2025)
Constrained Reinforcement Learning with Smoothed Log Barrier Function
by: Zhang, Baohe, et al.
Published: (2024)
by: Zhang, Baohe, et al.
Published: (2024)
Enhance Exploration in Safe Reinforcement Learning with Contrastive Representation Learning
by: Doan, Duc Kien, et al.
Published: (2025)
by: Doan, Duc Kien, et al.
Published: (2025)
Safe-Support Q-Learning: Learning without Unsafe Exploration
by: Lim, Yeeun, et al.
Published: (2026)
by: Lim, Yeeun, et al.
Published: (2026)
SafePred: A Predictive Guardrail for Computer-Using Agents via World Models
by: Chen, Yurun, et al.
Published: (2026)
by: Chen, Yurun, et al.
Published: (2026)
Efficient Exploration and Discriminative World Model Learning with an Object-Centric Abstraction
by: GX-Chen, Anthony, et al.
Published: (2024)
by: GX-Chen, Anthony, et al.
Published: (2024)
Local Mixtures of Experts: Essentially Free Test-Time Training via Model Merging
by: Bertolissi, Ryo, et al.
Published: (2025)
by: Bertolissi, Ryo, et al.
Published: (2025)
Optimistic Games for Combinatorial Bayesian Optimization with Application to Protein Design
by: Bal, Melis Ilayda, et al.
Published: (2024)
by: Bal, Melis Ilayda, et al.
Published: (2024)
Safe Reinforcement Learning for Real-World Engine Control
by: Bedei, Julian, et al.
Published: (2025)
by: Bedei, Julian, et al.
Published: (2025)
Test-Time Tuned Language Models Enable End-to-end De Novo Molecular Structure Generation from MS/MS Spectra
by: Mismetti, Laura, et al.
Published: (2025)
by: Mismetti, Laura, et al.
Published: (2025)
ContraLog: Log File Anomaly Detection with Contrastive Learning and Masked Language Modeling
by: Dietz, Simon, et al.
Published: (2026)
by: Dietz, Simon, et al.
Published: (2026)
SafeDreamer: Safe Reinforcement Learning with World Models
by: Huang, Weidong, et al.
Published: (2023)
by: Huang, Weidong, et al.
Published: (2023)
Verification-Guided Falsification for Safe RL via Explainable Abstraction and Risk-Aware Exploration
by: Le, Tuan, et al.
Published: (2025)
by: Le, Tuan, et al.
Published: (2025)
Large Language Models to Enhance Bayesian Optimization
by: Liu, Tennison, et al.
Published: (2024)
by: Liu, Tennison, et al.
Published: (2024)
MolWorld: Molecule World Models for Actionable Molecular Optimization
by: Qiao, Yang, et al.
Published: (2026)
by: Qiao, Yang, et al.
Published: (2026)
Similar Items
-
Active Few-Shot Fine-Tuning
by: Hübotter, Jonas, et al.
Published: (2024) -
Transductive Active Learning: Theory and Applications
by: Hübotter, Jonas, et al.
Published: (2024) -
Sample-efficient and Scalable Exploration in Continuous-Time RL
by: Iten, Klemens, et al.
Published: (2025) -
ActSafe: Active Exploration with Safety Constraints for Reinforcement Learning
by: As, Yarden, et al.
Published: (2024) -
Safe Exploration via Policy Priors
by: Wendl, Manuel, et al.
Published: (2026)