世界模型 - arXivDaily 专题

2402.05421 2025-06-16 cs.LG cs.AI cs.RO 64%

DiffTORI: Differentiable Trajectory Optimization for Deep Reinforcement and Imitation Learning

Weikang Wan, Ziyu Wang, Yufei Wang, Zackory Erickson, David Held

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

Comments NeurIPS 2024 (Spotlight)

2503.11964 2025-05-30 cs.LG stat.ML 64%

Entropy-regularized Gradient Estimators for Approximate Bayesian Inference

Jasmeet Kaur

机构 * Department of Computer Science University of Texas, Austin（计算机科学系德克萨斯大学奥斯汀分校）

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

2503.17693 2025-03-25 cs.NI 64%

Conditional Diffusion Model with OOD Mitigation as High-Dimensional Offline Resource Allocation Planner in Clustered Ad Hoc Networks

Kechen Meng, Sinuo Zhang, Rongpeng Li, Chan Wang, Ming Lei, Zhifeng Zhao

专题命中模型式强化学习：environment model(abstract)；model-based RL(abstract)；dynamics model(abstract)

Comments This work has been submitted to the IEEE for possible publication

2410.09972 2024-10-15 cs.LG cs.AI cs.CV cs.RO 64%

Make the Pertinent Salient: Task-Relevant Reconstruction for Visual Control with Distractions

Kyungmin Kim, JB Lanier, Pierre Baldi, Charless Fowlkes, Roy Fox

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

2209.10200 2023-07-12 cs.LG 64%

Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks

Sihua Wang, Mingzhe Chen, Christopher G. Brinton, Changchuan Yin, Walid Saad, Shuguang Cui

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

2301.11520 2023-06-01 cs.LG cs.AI cs.CV cs.RO 64%

SNeRL: Semantic-aware Neural Radiance Fields for Reinforcement Learning

Dongseok Shim, Seungjae Lee, H. Jin Kim

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

Comments ICML 2023. First two authors contributed equally. Order was determined by coin flip

2011.07401 2022-04-08 cs.PF cs.LG 64%

RL-QN: A Reinforcement Learning Framework for Optimal Control of Queueing Systems

Bai Liu, Qiaomin Xie, Eytan Modiano

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

2106.14421 2021-06-29 cs.LG 64%

Causal Reinforcement Learning using Observational and Interventional Data

Maxime Gasse, Damien Grasset, Guillaume Gaudron, Pierre-Yves Oudeyer

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

2008.12775 2021-05-28 cs.LG cs.AI cs.RO stat.ML 64%

On the model-based stochastic value gradient for continuous reinforcement learning

Brandon Amos, Samuel Stanton, Denis Yarats, Andrew Gordon Wilson

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

Comments L4DC 2021

2010.13957 2021-01-12 cs.LG cs.AI cs.CV cs.RO 64%

MELD: Meta-Reinforcement Learning from Images via Latent State Models

Tony Z. Zhao, Anusha Nagabandi, Kate Rakelly, Chelsea Finn, Sergey Levine

专题命中模型式强化学习：latent dynamics(abstract)；分类 cs.AI、cs.LG、cs.CV

Comments Accepted to CoRL 2020. Supplementary material at https://sites.google.com/view/meld-lsm/home . 16 pages, 19 figures. V2: add funding acknowledgements, reduce file size

2010.04893 2020-10-13 cs.LG 64%

Trust the Model When It Is Confident: Masked Model-based Actor-Critic

Feiyang Pan, Jia He, Dandan Tu, Qing He

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

Comments NeurIPS 2020

2004.08763 2020-04-21 cs.LG cs.AI cs.RO stat.ML 64%

Model-Predictive Control via Cross-Entropy and Gradient-Based Optimization

Homanga Bharadhwaj, Kevin Xie, Florian Shkurti

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

Comments L4DC 2020; Accepted for presentation in the 2nd Annual Conference on Learning for Dynamics and Control

1911.03594 2019-11-21 cs.LG cs.AI cs.RO stat.ML 64%

Robo-PlaNet: Learning to Poke in a Day

Maxime Chevalier-Boisvert, Guillaume Alain, Florian Golemo, Derek Nowrouzezahrai

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

Comments 4 pages, 3 figures. Version 2: added reference and acknowledgement

1902.00673 2019-02-05 cs.AI 64%

Belief dynamics extraction

Arun Kumar, Zhengwei Wu, Xaq Pitkow, Paul Schrater

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

1806.07371 2018-10-31 cs.CV cs.AI cs.LG 64%

Object-Oriented Dynamics Predictor

Guangxiang Zhu, Zhiao Huang, Chongjie Zhang

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

Comments Accepted to NIPS 2018

1708.02596 2017-12-05 cs.LG cs.AI cs.RO 64%

Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning

Anusha Nagabandi, Gregory Kahn, Ronald S. Fearing, Sergey Levine

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

1710.00489 2026-06-04 cs.RO cs.AI cs.CV cs.NE cs.SY eess.SY 62%

SE3-Pose-Nets: Structured Deep Dynamics Models for Visuomotor Planning and Control

SE3-姿态网络：用于视觉-运动规划和控制的结构深度动力学模型

Arunkumar Byravan, Felix Leeb, Franziska Meier, Dieter Fox

专题命中模型式强化学习：dynamics model(title,abstract)；分类 cs.AI、cs.CV、cs.RO

AI总结本文提出了一种基于结构深度动力学模型的深度视觉-运动控制方法，通过编码器-解码器结构学习低维姿态嵌入，实现场景分割和姿态预测，并在现实世界中实现了闭环控制。

Comments 8 pages, Initial submission to IEEE International Conference on Robotics and Automation (ICRA) 2018

2502.04892 2025-02-10 cs.LG q-bio.NC stat.ML 62%

A Foundational Brain Dynamics Model via Stochastic Optimal Control

Joonhyeong Park, Byoungwoo Park, Chang-Bae Bang, Jungwon Choi, Hyungjin Chung, Byung-Hoon Kim, Juho Lee

专题命中模型式强化学习：latent dynamics(abstract)；dynamics model(title)；分类 cs.LG

Comments The first two authors contributed equally

2312.04374 2024-12-03 cs.RO cs.AI cs.LG 62%

Deep Dynamics: Vehicle Dynamics Modeling with a Physics-Constrained Neural Network for Autonomous Racing

John Chrosniak, Jingyun Ning, Madhur Behl

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

Comments Published in the IEEE Robotics and Automation Letters and presented at the IEEE International Conference on Intelligent Robots and Systems

Journal ref IEEE Robotics and Automation Letters (Volume: 9, Issue: 6, June 2024), 5292 - 5297

2410.18912 2024-10-25 cs.RO cs.AI cs.LG 62%

Dynamic 3D Gaussian Tracking for Graph-Based Neural Dynamics Modeling

Mingtong Zhang, Kaifeng Zhang, Yunzhu Li

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

Comments Project Page: https://gs-dynamics.github.io

2407.01418 2024-07-02 cs.RO cs.AI cs.LG 62%

RoboPack: Learning Tactile-Informed Dynamics Models for Dense Packing

Bo Ai, Stephen Tian, Haochen Shi, Yixuan Wang, Cheston Tan, Yunzhu Li, Jiajun Wu

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

Comments Robotics: Science and Systems (RSS), 2024. Project page: https://robo-pack.github.io/

2309.06402 2023-09-13 q-bio.NC q-bio.QM 62%

Expressive dynamics models with nonlinear injective readouts enable reliable recovery of latent features from neural activity

Christopher Versteeg, Andrew R. Sedler, Jonathan D. McCart, Chethan Pandarinath

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

Comments 11 pages, 6 figures, Submitted to NeurIPS 2023

2210.00498 2023-02-23 cs.LG cs.AI cs.RO 62%

EUCLID: Towards Efficient Unsupervised Reinforcement Learning with Multi-choice Dynamics Model

Yifu Yuan, Jianye Hao, Fei Ni, Yao Mu, Yan Zheng, Yujing Hu, Jinyi Liu, Yingfeng Chen, Changjie Fan

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

Comments Published as a conference paper at ICLR 2023

2103.08255 2023-01-13 cs.LG cs.AI cs.RO 62%

Sample-efficient Reinforcement Learning Representation Learning with Curiosity Contrastive Forward Dynamics Model

Thanh Nguyen, Tung M. Luu, Thang Vu, Chang D. Yoo

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

Journal ref 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

2206.14802 2022-06-30 cs.RO cs.AI cs.LG 62%

Visual Foresight With a Local Dynamics Model

Colin Kohler, Robert Platt

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

2009.12864 2020-09-29 cs.LG cs.AI cs.RO 62%

Predicting Sim-to-Real Transfer with Probabilistic Dynamics Models

Lei M. Zhang, Matthias Plappert, Wojciech Zaremba

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

1907.04902 2019-07-12 cs.LG stat.ML 62%

Interpretable Dynamics Models for Data-Efficient Reinforcement Learning

Markus Kaiser, Clemens Otte, Thomas Runkler, Carl Henrik Ek

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

Comments ESANN 2019 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges (Belgium), 24-26 April 2019, i6doc.com publ., ISBN 978-287-587-065-0

1903.01599 2019-03-19 stat.ML cs.LG 62%

Learning Dynamics Model in Reinforcement Learning by Incorporating the Long Term Future

Nan Rosemary Ke, Amanpreet Singh, Ahmed Touati, Anirudh Goyal, Yoshua Bengio, Devi Parikh, Dhruv Batra

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

Comments To appear at ICLR 2019

2510.17709 2026-08-06 cs.LG cs.AI 版本更新 60%

Bi-Level Reinforcement Learning Pathway for Sim-to-Real Optimality

面向仿真到真实最优性的双层强化学习路径

Akhil S Anand, Shambhuraj Sawant, Paavo Parmas, Jasper Hoffmann, Dirk Reinhardt, Sebastien Gros

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

AI总结针对仿真到真实RL的目标不匹配问题，提出双层RL方法，通过分析策略对仿真参数的敏感性，结合真实策略性能梯度调整仿真模型，提升真实环境下的策略性能。

Journal ref RLJ, 2026

2601.14232 2026-07-02 cs.LG cs.AI cs.CV 版本更新 60%

KAGE-Bench: Fast Known-Axis Visual Generalization Evaluation for Reinforcement Learning

KAGE-Bench：面向强化学习的已知轴视觉泛化快速评估

Egor Cherepanov, Daniil Zelezetsky, Alexey K. Kovalev, Aleksandr I. Panov

机构 * AXXX, Moscow, Russia（AXXX，莫斯科，俄罗斯）； MIRAI, Moscow, Russia（MIRAI，莫斯科，俄罗斯）

专题命中模型式强化学习：latent dynamics(abstract)；分类 cs.AI、cs.LG、cs.CV

AI总结提出KAGE-Bench基准，通过解耦视觉轴独立评估像素策略在视觉分布偏移下的泛化能力，发现背景和光度偏移严重影响性能，而智能体外观偏移影响较小。

Comments 41 pages, 47 figures, 5 tables

1. 模型式强化学习 1126 篇

DiffTORI: Differentiable Trajectory Optimization for Deep Reinforcement and Imitation Learning

Entropy-regularized Gradient Estimators for Approximate Bayesian Inference

Conditional Diffusion Model with OOD Mitigation as High-Dimensional Offline Resource Allocation Planner in Clustered Ad Hoc Networks

Make the Pertinent Salient: Task-Relevant Reconstruction for Visual Control with Distractions

Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks

SNeRL: Semantic-aware Neural Radiance Fields for Reinforcement Learning

RL-QN: A Reinforcement Learning Framework for Optimal Control of Queueing Systems

Causal Reinforcement Learning using Observational and Interventional Data

On the model-based stochastic value gradient for continuous reinforcement learning

MELD: Meta-Reinforcement Learning from Images via Latent State Models

Trust the Model When It Is Confident: Masked Model-based Actor-Critic

Model-Predictive Control via Cross-Entropy and Gradient-Based Optimization

Robo-PlaNet: Learning to Poke in a Day

Belief dynamics extraction

Object-Oriented Dynamics Predictor

Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning

SE3-Pose-Nets: Structured Deep Dynamics Models for Visuomotor Planning and Control

A Foundational Brain Dynamics Model via Stochastic Optimal Control

Deep Dynamics: Vehicle Dynamics Modeling with a Physics-Constrained Neural Network for Autonomous Racing

Dynamic 3D Gaussian Tracking for Graph-Based Neural Dynamics Modeling

RoboPack: Learning Tactile-Informed Dynamics Models for Dense Packing

Expressive dynamics models with nonlinear injective readouts enable reliable recovery of latent features from neural activity

EUCLID: Towards Efficient Unsupervised Reinforcement Learning with Multi-choice Dynamics Model

Sample-efficient Reinforcement Learning Representation Learning with Curiosity Contrastive Forward Dynamics Model

Visual Foresight With a Local Dynamics Model

Predicting Sim-to-Real Transfer with Probabilistic Dynamics Models

Interpretable Dynamics Models for Data-Efficient Reinforcement Learning

Learning Dynamics Model in Reinforcement Learning by Incorporating the Long Term Future

Bi-Level Reinforcement Learning Pathway for Sim-to-Real Optimality

KAGE-Bench: Fast Known-Axis Visual Generalization Evaluation for Reinforcement Learning