Imagen de Portada

Uncertainty-Based Smooth Policy Regularisation for Reinforcement Learning with Few Demonstrations

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Zhu, Yujie, Hepburn, Charles A., Thorpe, Matthew, Montana, Giovanni
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning Artificial Intelligence Robotics
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

State-Constrained Offline Reinforcement Learning
por: Hepburn, Charles A., et al.
Publicado: (2024)

Robot Policy Transfer with Online Demonstrations: An Active Reinforcement Learning Approach
por: Hou, Muhan, et al.
Publicado: (2025)

REValueD: Regularised Ensemble Value-Decomposition for Factorisable Markov Decision Processes
por: Ireland, David, et al.
Publicado: (2024)

DreamSmooth: Improving Model-based Reinforcement Learning via Reward Smoothing
por: Lee, Vint, et al.
Publicado: (2023)

RLDG: Robotic Generalist Policy Distillation via Reinforcement Learning
por: Xu, Charles, et al.
Publicado: (2024)

Reverse Forward Curriculum Learning for Extreme Sample and Demonstration Efficiency in Reinforcement Learning
por: Tao, Stone, et al.
Publicado: (2024)

Offline-to-online Reinforcement Learning for Image-based Grasping with Scarce Demonstrations
por: Chan, Bryan, et al.
Publicado: (2024)

Learning Variable Compliance Control From a Few Demonstrations for Bimanual Robot with Haptic Feedback Teleoperation System
por: Kamijo, Tatsuya, et al.
Publicado: (2024)

Accelerating Residual Reinforcement Learning with Uncertainty Estimation
por: Dodeja, Lakshita, et al.
Publicado: (2025)

Hierarchical Reinforcement Learning for Swarm Confrontation with High Uncertainty
por: Wu, Qizhen, et al.
Publicado: (2024)

Evaluation-Time Policy Switching for Offline Reinforcement Learning
por: Neggatu, Natinael Solomon, et al.
Publicado: (2025)

Efficient and Generalized end-to-end Autonomous Driving System with Latent Deep Reinforcement Learning and Demonstrations
por: Tang, Zuojin, et al.
Publicado: (2024)

CLAM: Continuous Latent Action Models for Robot Learning from Unlabeled Demonstrations
por: Liang, Anthony, et al.
Publicado: (2025)

Rethinking Policy Diversity in Ensemble Policy Gradient in Large-Scale Reinforcement Learning
por: Shitanda, Naoki, et al.
Publicado: (2026)

ReinforceGen: Hybrid Skill Policies with Automated Data Generation and Reinforcement Learning
por: Zhou, Zihan, et al.
Publicado: (2025)

Mutual Information Tracks Policy Coherence in Reinforcement Learning
por: Reid, Cameron, et al.
Publicado: (2025)

Uncertainty-Aware Robotic World Model Makes Offline Model-Based Reinforcement Learning Work on Real Robots
por: Li, Chenhao, et al.
Publicado: (2025)

Learning Hybrid-Control Policies for High-Precision In-Contact Manipulation Under Uncertainty
por: Brown, Hunter L., et al.
Publicado: (2026)

REFINE-DP: Diffusion Policy Fine-tuning for Humanoid Loco-manipulation via Reinforcement Learning
por: Gu, Zhaoyuan, et al.
Publicado: (2026)

Diffusion Policies with Value-Conditional Optimization for Offline Reinforcement Learning
por: Ma, Yunchang, et al.
Publicado: (2025)

Adaptive Advantage-Guided Policy Regularization for Offline Reinforcement Learning
por: Liu, Tenglong, et al.
Publicado: (2024)

Dual Action Policy for Robust Sim-to-Real Reinforcement Learning
por: Terence, Ng Wen Zheng, et al.
Publicado: (2024)

Learning Parameterized Skills from Demonstrations
por: Gupta, Vedant, et al.
Publicado: (2025)

Safe Offline Reinforcement Learning with Feasibility-Guided Diffusion Model
por: Zheng, Yinan, et al.
Publicado: (2024)

FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning
por: Alles, Marvin, et al.
Publicado: (2025)

Exploiting Hybrid Policy in Reinforcement Learning for Interpretable Temporal Logic Manipulation
por: Zhang, Hao, et al.
Publicado: (2024)

Leveraging Sub-Optimal Data for Human-in-the-Loop Reinforcement Learning
por: Muslimani, Calarina, et al.
Publicado: (2024)

Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why
por: Li, Chenhao, et al.
Publicado: (2025)

Towards Robust Policy: Enhancing Offline Reinforcement Learning with Adversarial Attacks and Defenses
por: Nguyen, Thanh, et al.
Publicado: (2024)

Enhancing Hardware Fault Tolerance in Machines with Reinforcement Learning Policy Gradient Algorithms
por: Schoepp, Sheila, et al.
Publicado: (2024)

Sampling-Based Safe Reinforcement Learning
por: Vignola, Luca, et al.
Publicado: (2026)

Learning Adaptive Dexterous Grasping from Single Demonstrations
por: Shi, Liangzhi, et al.
Publicado: (2025)

Subwords as Skills: Tokenization for Sparse-Reward Reinforcement Learning
por: Yunis, David, et al.
Publicado: (2023)

Learning Novel Skills from Language-Generated Demonstrations
por: Jin, Ao-Qun, et al.
Publicado: (2024)

Enhancing Robot Navigation Policies with Task-Specific Uncertainty Managements
por: Puthumanaillam, Gokul, et al.
Publicado: (2025)

Adaptive Target Localization under Uncertainty using Multi-Agent Deep Reinforcement Learning with Knowledge Transfer
por: Alagha, Ahmed, et al.
Publicado: (2025)

Continual Policy Distillation of Reinforcement Learning-based Controllers for Soft Robotic In-Hand Manipulation
por: Li, Lanpei, et al.
Publicado: (2024)

A Clean Slate for Offline Reinforcement Learning
por: Jackson, Matthew Thomas, et al.
Publicado: (2025)

Performance Asymmetry in Model-Based Reinforcement Learning
por: Lim, Jing Yu, et al.
Publicado: (2025)

Continual Model-Based Reinforcement Learning with Hypernetworks
por: Huang, Yizhou, et al.
Publicado: (2020)