世界模型

Model-based Reinforcement Learning with a Hamiltonian Canonical ODE Network

Yao Feng, Yuhong Jiang, Hang Su, Dong Yan, Jun Zhu

专题命中 模型式强化学习 ：model-based reinforcement learning(title,abstract)；world model(abstract)；world model(abstract)；environment model(abstract)

Performance Asymmetry in Model-Based Reinforcement Learning

基于模型的强化学习中的性能不对称性

Jing Yu Lim, Rushi Shah, Zarif Ikram, Samson Yu, Haozhe Ma, Tze-Yun Leong, Dianbo Liu

机构 * Department of XXX, University of YYY, Location, Country（XXX系，YYY大学，地点，国家） ； School of ZZZ, Institute of WWW, Location, Country（ZZZ学院，WWW研究所，地点，国家） ； National University of Singapore（新加坡国立大学） 纠错

专题命中 模型式强化学习 ：model-based reinforcement learning(title,abstract)；world model(abstract)；world model(abstract)；分类 cs.AI、cs.LG、cs.RO

AI总结 本文提出JEDI世界模型，通过解决基于模型的强化学习中的性能不对称问题，在Human-Optimal任务和Breakout上取得最优成绩，同时提升计算效率。

Comments Preprint

Dream to Generalize: Zero-Shot Model-Based Reinforcement Learning for Unseen Visual Distractions

Jeongsoo Ha, Kyungsoo Kim, Yusung Kim

专题命中 模型式强化学习 ：model-based reinforcement learning(title,abstract)；world model(abstract)；world model(abstract)；分类 cs.AI、cs.CV

Comments AAAI 2023

Sense, Imagine, Act: Multimodal Perception Improves Model-Based Reinforcement Learning for Head-to-Head Autonomous Racing

Elena Shrestha, Chetan Reddy, Hanxi Wan, Yulun Zhuang, Ram Vasudevan

专题命中 模型式强化学习 ：model-based reinforcement learning(title,abstract)；world model(abstract)；world model(abstract)；分类 cs.AI、cs.LG、cs.RO

MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations

Nicklas Hansen, Yixin Lin, Hao Su, Xiaolong Wang, Vikash Kumar, Aravind Rajeswaran

专题命中 模型式强化学习 ：model-based reinforcement learning(title)；world model(abstract)；world model(abstract)；model-based RL(abstract)

Learning a Particle Dynamics Model with Real-world Videos

利用真实世界视频学习粒子动力学模型

Chanho Kim, Suhas V. Sumukh, Li Fuxin

机构 * Oregon State University（俄勒冈州立大学） 纠错

专题命中 模型式强化学习 ：world model(abstract)；world models(abstract)；dynamics model(title,abstract)；world model(abstract)

AI总结 提出一种从无标签真实视频直接训练神经物体动力学模型的框架，结合高斯溅射技术，通过渲染监督学习粒子位置和旋转变化，避免对合成数据的依赖。

Comments CVPR 2026 Findings

Factorized Latent Dynamics for Video JEPA: An Empirical Study of Auxiliary Objectives

视频JEPA中的因子化潜在动态：辅助目标的实证研究

Santosh Premi

机构 * Adhikari（阿迪卡里） 纠错

专题命中 模型式强化学习 ：latent dynamics(title,abstract)；world-model(abstract)；world-model(abstract)；分类 cs.LG、cs.CV

AI总结 本研究探讨了视频JEPA中辅助目标的实证效果，通过对比不同辅助目标变体，发现潜在表示的因子化方法在提升某些能力的同时可能降低其他能力，FWM-HW-LD在混合数据集下提升了ImageNet-100和SSv2的性能。

Scaling Tasks, Not Samples: Mastering Humanoid Control through Multi-Task Model-Based Reinforcement Learning

通过多任务模型基于强化学习掌握人形控制

Shaohuai Liu, Weirui Ye, Yilun Du, Le Xie

专题命中 模型式强化学习 ：model-based reinforcement learning(title,abstract)；world model(abstract)；world model(abstract)；分类 cs.AI、cs.RO

AI总结 本文提出EfficientZero-Multitask算法，通过多任务模型基于强化学习提升人形机器人控制性能，实现高效样本利用和高任务适应性。

Scaling Offline Model-Based RL via Jointly-Optimized World-Action Model Pretraining

通过联合优化的世界-动作模型扩展离线模型基于的强化学习

Jie Cheng, Ruixi Qiao, Yingwei Ma, Binhua Li, Gang Xiong, Qinghai Miao, Yongbin Li, Yisheng Lv

机构 * State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，自动化研究所，中国科学院） ； School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院） ； Alibaba Group（阿里巴巴集团） 纠错

专题命中 模型式强化学习 ：model-based RL(title,abstract)；world model(abstract)；world model(abstract)；分类 cs.AI、cs.LG

AI总结 JOWA通过联合优化的世界-动作模型扩展离线RL，实现高效泛化和高性能

Comments Accepted by ICLR 2025

medDreamer: Model-Based Reinforcement Learning with Latent Imagination on Complex EHRs for Clinical Decision Support

medDreamer：基于复杂电子病历的潜在想象的模型驱动强化学习用于临床决策支持

Qianyi Xu, Gousia Habib, Feng Wu, Dilruk Perera, Mengling Feng

机构 * National University of Singapore（新加坡国立大学） 纠错

专题命中 模型式强化学习 ：model-based reinforcement learning(title,abstract)；world model(abstract)；world model(abstract)；分类 cs.AI、cs.LG

AI总结 medDreamer通过结合潜在想象和自适应特征集成模块，实现基于复杂电子病历的模型驱动强化学习，以提升个性化治疗推荐的性能。

Policy-shaped prediction: avoiding distractions in model-based reinforcement learning

Miles Hutson, Isaac Kauvar, Nick Haber

专题命中 模型式强化学习 ：model-based reinforcement learning(title,abstract)；world model(abstract)；world model(abstract)；分类 cs.AI、cs.LG

Comments Accepted at NeurIPS 2024

Mind the Model, Not the Agent: The Primacy Bias in Model-based RL

Zhongjian Qiao, Jiafei Lyu, Xiu Li

专题命中 模型式强化学习 ：model-based RL(title)；world model(abstract)；world model(abstract)；model-based reinforcement learning(abstract)

Comments Accepted by European Conference on Artificial Intelligence (ECAI) 2024

Prototypical context-aware dynamics generalization for high-dimensional model-based reinforcement learning

Junjie Wang, Yao Mu, Dong Li, Qichao Zhang, Dongbin Zhao, Yuzheng Zhuang, Ping Luo, Bin Wang, Jianye Hao

专题命中 模型式强化学习 ：model-based reinforcement learning(title)；world model(abstract)；world model(abstract)；model-based RL(abstract)

Scalable Multi-Agent Model-Based Reinforcement Learning

Vladimir Egorov, Aleksei Shpilman

专题命中 模型式强化学习 ：model-based reinforcement learning(title,abstract)；world model(abstract)；world model(abstract)；分类 cs.AI、cs.LG

Comments AAMAS'2022, cite https://dl.acm.org/doi/abs/10.5555/3535850.3535894

DreamerPro: Reconstruction-Free Model-Based Reinforcement Learning with Prototypical Representations

Fei Deng, Ingook Jang, Sungjin Ahn

专题命中 模型式强化学习 ：model-based reinforcement learning(title,abstract)；world model(abstract)；world model(abstract)；分类 cs.AI、cs.LG

Fractional Transfer Learning for Deep Model-Based Reinforcement Learning

Remo Sasso, Matthia Sabatelli, Marco A. Wiering

专题命中 模型式强化学习 ：model-based reinforcement learning(title)；world model(abstract)；world model(abstract)；model-based RL(abstract)

Comments 21 pages, 8 figures, 7 tables

Unlocking Temporal Generalization in Hamiltonian Video Dynamics Models

解锁哈密顿视频动力学模型中的时间泛化

Eli Laird, Corey Clark

机构 * Department of Computer Science, Southern Methodist University（南卫理公会大学计算机科学系） 纠错

专题命中 模型式强化学习 ：world model(abstract)；world models(abstract)；world model(abstract)；world models(abstract)

AI总结 研究世界模型在可变时间分辨率下预测动力学的问题，利用哈密顿生成网络（HGN），指出其在非保守环境中时间泛化失效的问题及原因，通过针对性修复实现稳定动力学预测，推荐连续时间视频生成中时间泛化的策略。

Comments To appear in the 1st Workshop on Physics-Aware Video Generation and Restoration at the 28th International Conference on Pattern Recognition

DynaWeb: Model-Based Reinforcement Learning of Web Agents

DynaWeb：基于模型的Web代理强化学习

Hang Ding, Peidong Liu, Junqiao Wang, Ziwei Ji, Meng Cao, Rongzhao Zhang, Lynn Ai, Eric Yang, Tianyu Shi, Lei Yu

机构 * Shanghai Jiao Tong University（上海交通大学） ； Sichuan University（四川大学） ； Hong Kong University of Science and Technology（香港科学与技术大学） ； McGill University（麦吉尔大学） ； Shanghai AI Lab（上海人工智能实验室） ； Gradient ； University of Toronto（多伦多大学） ； Mila - Quebec AI Institute（魁北克AI研究所） 纠错

专题命中 模型式强化学习 ：model-based reinforcement learning(title,abstract)；world model(abstract)；world model(abstract)；分类 cs.AI

AI总结 本文提出DynaWeb框架，通过模拟环境提升Web代理的强化学习效率，实验表明其在WebArena和WebVoyager基准上显著提升性能。

SkyDreamer: Interpretable End-to-End Vision-Based Drone Racing with Model-Based Reinforcement Learning

Aderik Verraest, Stavrow Bahnam, Robin Ferede, Guido de Croon, Christophe De Wagter

机构 * Faculty of Aerospace Engineering, Delft University of Technology（航空航天工程学院，代尔夫特理工大学） 纠错

专题命中 模型式强化学习 ：model-based reinforcement learning(title,abstract)；world model(abstract)；world model(abstract)；分类 cs.RO

Informed POMDP: Leveraging Additional Information in Model-Based RL

Gaspard Lambrechts, Adrien Bolland, Damien Ernst

专题命中 模型式强化学习 ：model-based RL(title,abstract)；world model(abstract)；world model(abstract)；分类 cs.LG

Comments In Reinforcement Learning Conference, 2024. 10 pages, 22 pages total, 10 figures

Journal ref Reinforcement Learning Journal, 2024

INFOrmation Prioritization through EmPOWERment in Visual Model-Based RL

Homanga Bharadhwaj, Mohammad Babaeizadeh, Dumitru Erhan, Sergey Levine

专题命中 模型式强化学习 ：model-based RL(title,abstract)；model-based reinforcement learning(abstract)；model based RL(abstract)；分类 cs.AI、cs.LG、cs.RO

Comments Published in International Conference on Learning Representations (ICLR 2022)

Biased Dreams: Limitations to Epistemic Uncertainty Quantification in Latent Dynamics Models

有偏的梦境：潜在空间模型中知识不确定性量化限制

Julia Berger, Bernd Frauenknecht, Sebastian Trimpe, Bastian Leibe

专题命中 模型式强化学习 ：latent dynamics(title,abstract)；dynamics model(title,abstract)；model-based reinforcement learning(abstract)；分类 cs.LG

AI总结 研究探讨了潜在空间模型中知识不确定性量化存在的偏差问题，指出潜在过渡偏向于高代表性的区域，导致环境动态差异在潜在空间中不显现，影响不确定性估计的可靠性。

Comments Reinforcement Learning Conference (RLC) 2026

Imagined Value Gradients: Model-Based Policy Optimization with Transferable Latent Dynamics Models

Arunkumar Byravan, Jost Tobias Springenberg, Abbas Abdolmaleki, Roland Hafner, Michael Neunert, Thomas Lampe, Noah Siegel, Nicolas Heess, Martin Riedmiller

专题命中 模型式强化学习 ：latent dynamics(title)；model-based reinforcement learning(abstract)；dynamics model(title)；分类 cs.AI、cs.LG、cs.CV

Comments To appear at the 3rd annual Conference on Robot Learning, Osaka, Japan (CoRL 2019). 24 pages including appendix (main paper - 8 pages)

Multi-Agent Model-Based Reinforcement Learning with Joint State-Action Learned Embeddings

基于联合状态-动作学习嵌入的多智能体模型驱动强化学习

Zhizun Wang, David Meger

机构 * McGill University（麦吉尔大学） 纠错

专题命中 模型式强化学习 ：model-based reinforcement learning(title)；world model(abstract)；world model(abstract)；分类 cs.LG、cs.MA

AI总结 本文提出了一种基于联合状态-动作学习嵌入的多智能体模型驱动强化学习框架，通过统一表示学习与想象式回放，提升智能体在动态环境中协调能力与长期规划效率。

Comments 22 pages

Robust Model-Based Reinforcement Learning with an Adversarial Auxiliary Model

Siemen Herremans, Ali Anwar, Siegfried Mercelis

专题命中 模型式强化学习 ：model-based reinforcement learning(title)；world model(abstract)；world model(abstract)；分类 cs.AI、cs.LG

Comments Will be presented at the RL Safety Workshop at RLC 2024

Predictable MDP Abstraction for Unsupervised Model-Based RL

Seohong Park, Sergey Levine

专题命中 模型式强化学习 ：model-based RL(title,abstract)；model-based reinforcement learning(abstract)；分类 cs.AI、cs.LG；dynamics model(abstract)

Comments ICML 2023

Intrinsic Motivation in Model-based Reinforcement Learning: A Brief Review

Artem Latyshev, Aleksandr I. Panov

专题命中 模型式强化学习 ：model-based reinforcement learning(title)；world model(abstract)；world model(abstract)；分类 cs.AI、cs.LG

Comments 13 pages, 7 figures

Skill-based Model-based Reinforcement Learning

Lucy Xiaoyang Shi, Joseph J. Lim, Youngwoon Lee

专题命中 模型式强化学习 ：model-based reinforcement learning(title,abstract)；model-based RL(abstract)；分类 cs.AI、cs.LG、cs.RO；dynamics model(abstract)

Comments Published at the Conference on Robot Learning (CoRL) 2022. Website: https://clvrai.com/skimo

Model-based Reinforcement Learning for Decentralized Multiagent Rendezvous

Rose E. Wang, J. Chase Kew, Dennis Lee, Tsang-Wei Edward Lee, Tingnan Zhang, Brian Ichter, Jie Tan, Aleksandra Faust

专题命中 模型式强化学习 ：model-based reinforcement learning(title,abstract)；model-based RL(abstract)；分类 cs.AI、cs.LG、cs.RO

Comments CoRL 2020. The video is available at: https://youtu.be/-ydXHUtPzWE

Dynamic Horizon Value Estimation for Model-based Reinforcement Learning

Junjie Wang, Qichao Zhang, Dongbin Zhao, Mengchen Zhao, Jianye Hao

专题命中 模型式强化学习 ：model-based reinforcement learning(title)；world model(abstract)；world model(abstract)；分类 cs.AI、cs.LG

Comments 9 pages, 6 figures