Imagen de Portada

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Chen, Yangkun, Yang, Kai, Tao, Jian, Lyu, Jiafei
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

A Two-stage Reinforcement Learning-based Approach for Multi-entity Task Allocation
por: Gong, Aicheng, et al.
Publicado: (2024)

Exploration and Anti-Exploration with Distributional Random Network Distillation
por: Yang, Kai, et al.
Publicado: (2024)

Novelty-based Sample Reuse for Continuous Robotics Control
por: Duan, Ke, et al.
Publicado: (2024)

Efficient Cross-Domain Offline Reinforcement Learning with Dynamics- and Value-Aligned Data Filtering
por: Qiao, Zhongjian, et al.
Publicado: (2025)

Debiased Model-based Representations for Sample-efficient Continuous Control
por: Lyu, Jiafei, et al.
Publicado: (2026)

EntroPIC: Towards Stable Long-Term Training of LLMs via Entropy Stabilization with Proportional-Integral Control
por: Yang, Kai, et al.
Publicado: (2025)

Mildly Conservative Q-Learning for Offline Reinforcement Learning
por: Lyu, Jiafei, et al.
Publicado: (2022)

ADG: Ambient Diffusion-Guided Dataset Recovery for Corruption-Robust Offline Reinforcement Learning
por: Liu, Zeyuan, et al.
Publicado: (2025)

State-Novelty Guided Action Persistence in Deep Reinforcement Learning
por: Hu, Jianshu, et al.
Publicado: (2024)

Exploration by Random Distribution Distillation
por: Fang, Zhirui, et al.
Publicado: (2025)

Understanding What Affects the Generalization Gap in Visual Reinforcement Learning: Theory and Empirical Evidence
por: Lyu, Jiafei, et al.
Publicado: (2024)

Mind the Model, Not the Agent: The Primacy Bias in Model-based RL
por: Qiao, Zhongjian, et al.
Publicado: (2023)

SUMO: Search-Based Uncertainty Estimation for Model-Based Offline Reinforcement Learning
por: Qiao, Zhongjian, et al.
Publicado: (2024)

A Large Language Model-Driven Reward Design Framework via Dynamic Feedback for Reinforcement Learning
por: Sun, Shengjie, et al.
Publicado: (2024)

Dual-Robust Cross-Domain Offline Reinforcement Learning Against Dynamics Shifts
por: Qiao, Zhongjian, et al.
Publicado: (2025)

Unifying Value Alignment and Assignment in Cross-Domain Offline Reinforcement Learning with Heterogeneous Datasets
por: Qiao, Zhongjian, et al.
Publicado: (2026)

Optimizing Novelty of Top-k Recommendations using Large Language Models and Reinforcement Learning
por: Sharma, Amit, et al.
Publicado: (2024)

On the Reuse Bias in Off-Policy Reinforcement Learning
por: Ying, Chengyang, et al.
Publicado: (2022)

Learning to Reflect: Hierarchical Multi-Agent Reinforcement Learning for CSI-Free mmWave Beam-Focusing
por: Le, Hieu, et al.
Publicado: (2026)

Multi-Agent Deep Reinforcement Learning for Energy Efficient Multi-Hop STAR-RIS-Assisted Transmissions
por: Liao, Pei-Hsiang, et al.
Publicado: (2024)

LVNS-RAVE: Diversified audio generation with RAVE and Latent Vector Novelty Search
por: Guo, Jinyue, et al.
Publicado: (2024)

Preference-Guided Reinforcement Learning for Efficient Exploration
por: Wang, Guojian, et al.
Publicado: (2024)

From Novelty to Imitation: Self-Distilled Rewards for Offline Reinforcement Learning
por: Chaudhary, Gaurav, et al.
Publicado: (2025)

Novelty Detection in Reinforcement Learning with World Models
por: Zollicoffer, Geigh, et al.
Publicado: (2023)

Diversified Batch Selection for Training Acceleration
por: Hong, Feng, et al.
Publicado: (2024)

ODRL: A Benchmark for Off-Dynamics Reinforcement Learning
por: Lyu, Jiafei, et al.
Publicado: (2024)

Model-based Offline RL via Robust Value-Aware Model Learning with Implicitly Differentiable Adaptive Weighting
por: Qiao, Zhongjian, et al.
Publicado: (2026)

Traversing Pareto Optimal Policies: Provably Efficient Multi-Objective Reinforcement Learning
por: Qiu, Shuang, et al.
Publicado: (2024)

Preference-Guided Learning for Sparse-Reward Multi-Agent Reinforcement Learning
por: Bui, The Viet, et al.
Publicado: (2025)

Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance
por: He, Jinmin, et al.
Publicado: (2025)

An Arbitration Control for an Ensemble of Diversified DQN variants in Continual Reinforcement Learning
por: Jang, Wonseo, et al.
Publicado: (2025)

Efficient Reinforcement Learning by Guiding Generalist World Models with Non-Curated Data
por: Zhao, Yi, et al.
Publicado: (2025)

PEARL: Zero-shot Cross-task Preference Alignment and Robust Reward Learning for Robotic Manipulation
por: Liu, Runze, et al.
Publicado: (2023)

Rollout-Training Co-Design for Efficient LLM-Based Multi-Agent Reinforcement Learning
por: Jiang, Zhida, et al.
Publicado: (2026)

An Efficient Open World Environment for Multi-Agent Social Learning
por: Ye, Eric, et al.
Publicado: (2025)

Settling Decentralized Multi-Agent Coordinated Exploration by Novelty Sharing
por: Jiang, Haobin, et al.
Publicado: (2024)

Efficient Multi-Policy Evaluation for Reinforcement Learning
por: Liu, Shuze Daniel, et al.
Publicado: (2024)

RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment
por: Yang, Suorong, et al.
Publicado: (2025)

Using Human Feedback to Fine-tune Diffusion Models without Any Reward Model
por: Yang, Kai, et al.
Publicado: (2023)

LLM-Guided Communication for Cooperative Multi-Agent Reinforcement Learning
por: Bae, Sangjun, et al.
Publicado: (2026)