世界模型

Model-based Reinforcement Learning and the Eluder Dimension

Ian Osband, Benjamin Van Roy

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

Traffic expertise meets residual RL: Knowledge-informed model-based residual reinforcement learning for CAV trajectory control

交通专家与残差强化学习相遇：基于知识的模型驱动残差强化学习用于智能交通车辆轨迹控制

Zihao Sheng, Zilin Huang, Sikai Chen

机构 * Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, WI, 53706, USA（土木与环境工程系，威斯尼大学麦迪逊分校） 纠错

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

AI总结 本文提出基于知识的模型驱动残差强化学习框架，用于智能交通车辆轨迹控制，结合交通专家知识与传统控制方法，提升学习效率与交通流平滑度。

Comments Accepted by Communications in Transportation Research

Journal ref Communications in Transportation Research 4 (2024): 100142

Learning Sequential Latent Variable Models from Multimodal Time Series Data

Oliver Limoyo, Trevor Ablett, Jonathan Kelly

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

Comments In: Petrovic, I., Menegatti, E., Marković, I. (eds) Intelligent Autonomous Systems 17. IAS 2022. Lecture Notes in Networks and Systems, vol 577. Springer, Cham

Uncovering Latent Structures in Robust Pulse Sequences: A Model-Based Reinforcement Learning Approach for Adaptable Quantum Control

揭示鲁棒脉冲序列中的潜在结构：一种基于模型的强化学习方法用于自适应量子控制

Tobias Kiermeyer, Thomas Heydenreich, Léo Van Damme, Sebastian Hohenemser, Florian Marquardt, Steffen J. Glaser

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

AI总结 提出一种基于模型强化学习的鲁棒最优量子控制方法，通过将哈密顿量嵌入训练管道的神经网络，无需预计算数据即可为整个门族生成鲁棒脉冲，在单自旋系统上实现毫秒级任意旋转角脉冲，保真度媲美多种子GRAPE，并发现控制景观中的结构化相位轮廓。

Comments 14 pages, 8 figures

Model-based reinforcement learning in differential graphical games

基于微分图游戏的模型引导强化学习

Rushikesh Kamalapurkar, Justin R. Klotz, Patrick Walters, Warren E. Dixon

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

AI总结 本文结合微分博弈理论与actor-critic-identifier架构，提出连续控制策略以实现多智能体系统编队跟踪，通过通信拓扑中的扩展邻居反馈设计近似最优控制器，仿真验证了该方法的有效性。

Model-based reinforcement learning for infinite-horizon approximate optimal tracking

基于模型的强化学习用于无限 horizon 近似最优跟踪

Rushikesh Kamalapurkar, Lindsey Andrews, Patrick Walters, Warren E. Dixon

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

AI总结 本文提出了一种近似在线自适应解决方案，用于解决具有未知漂移动力学的连续时间非线性系统的无限 horizon 最优跟踪问题，通过基于模型的强化学习放松持续激励条件，并通过Lyapunov稳定性分析证明了策略收敛性。

Model Based Reinforcement Learning with Final Time Horizon Optimization

基于最终时间 horizon 的模型驱动强化学习

Wei Sun, Evangelos Theodorou, Panagiotis Tsiotras

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

AI总结 本文提出一种基于模型的强化学习与轨迹优化算法，通过最优控制理论和动态规划推导出反向微分方程，提供最优控制策略和时间 horizon。在低维线性问题中恢复理论最优解，并在非线性系统中验证应用效果。

Comments 9 pages, 5 figures, NIPS2015

A Neural Latent Dynamics Approach for Solving Inverse Problems in Cardiac Electrophysiology

用于心脏电生理学逆问题的神经潜在动态方法

Edoardo Centofanti, Giovanni Ziarelli, Simone Scacchi, Luca Franco Pavarino

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

AI总结 本文提出利用潜在动态网络构建高效代理模型，解决心脏电生理学逆问题中的参数恢复难题，通过神经微分方程实现低维参数到ECG信号的映射，减少计算负担并提升临床应用效率。

Comments 29 pages, 9 figures

Event-Level Probabilistic Prediction of Extreme Rainfall over India Using Physics-Gated Latent Dynamics

基于物理门控隐动态的印度极端降雨事件级概率预测

Arun Govind Neelan

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

AI总结 本研究提出物理门控隐动态框架，用于印度极端降雨事件的事件级概率预测，通过改进模型在极端事件检测和预测中的性能。

Adaptive Model-Based Reinforcement Learning for Orbit Feedback Control in NSLS-II Storage Ring

自适应模型驱动强化学习用于NSLS-II存储环轨道反馈控制

Zeyu Dong, Yuke Tian, Yu Sun

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

AI总结 本文提出基于模型驱动强化学习的自适应框架，用于NSLS-II存储环轨道反馈控制，通过轨迹优化和在线模型优化实现束流稳定与对准误差最小化。

Comments Accepted by the 20th International Conference on Accelerator and Large Experimental Physics Control Systems (ICALEPCS 2025)

Safe model-based Reinforcement Learning via Model Predictive Control and Control Barrier Functions

通过模型预测控制和控制屏障函数实现安全的模型基于强化学习

Kerim Dzhumageldyev, Filippo Airaldi, Azita Dabiri

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

AI总结 本文提出一种安全的模型基于强化学习框架，结合模型预测控制与控制屏障函数，通过参数化方法改进安全控制策略。

Comments Submitted to IFAC WC 2026, 7 pages, 3 figures

Improved Greedy Identification of Latent Dynamics with Application to Fluid Flows

R. Ayoub, M. Oulghelou, P. J Schmid

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

The Edge-of-Reach Problem in Offline Model-Based Reinforcement Learning

Anya Sims, Cong Lu, Jakob Foerster, Yee Whye Teh

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

Comments Code open-sourced at: https://github.com/anyasims/edge-of-reach

Journal ref NeurIPS 2024

A Safe and Data-efficient Model-based Reinforcement Learning System for HVAC Control

Xianzhong Ding, Zhiyu An, Arya Rathee, Wan Du

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

Learning the Latent dynamics of Fluid flows from High-Fidelity Numerical Simulations using Parsimonious Diffusion Maps

Alessandro Della Pia, Dimitris Patsatzis, Lucia Russo, Constantinos Siettos

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

Journal ref Physics of Fluids 36, 105187 (2024)

Exploiting Symmetry in Dynamics for Model-Based Reinforcement Learning with Asymmetric Rewards

Yasin Sonmez, Neelay Junnarkar, Murat Arcak

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

FLD: Fourier Latent Dynamics for Structured Motion Representation and Learning

Chenhao Li, Elijah Stanger-Jones, Steve Heim, Sangbae Kim

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

Data-Driven Characterization of Latent Dynamics on Quantum Testbeds

Sohail Reddy, Stefanie Guenther, Yujin Cho

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

Live in the Moment: Learning Dynamics Model Adapted to Evolving Policy

Xiyao Wang, Wichayaporn Wongkamjan, Furong Huang

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

Comments 16 pages, 5 figures

Journal ref ICML 2023

Model Based Reinforcement Learning with Non-Gaussian Environment Dynamics and its Application to Portfolio Optimization

Huifang Huang, Ting Gao, Pengbo Li, Jin Guo, Peng Zhang, Nan Du

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

Comments arXiv admin note: text overlap with arXiv:2205.15056

Reduced models with nonlinear approximations of latent dynamics for model premixed flame problems

Wayne Isaac Tan Uy, Christopher R. Wentland, Cheng Huang, Benjamin Peherstorfer

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

A Model-Based Reinforcement Learning Approach for PID Design

Hozefa Jesawada, Amol Yerudkar, Carmen Del Vecchio, Navdeep Singh

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

Safe Controller for Output Feedback Linear Systems using Model-Based Reinforcement Learning

S M Nahid Mahmud, Moad Abudia, Scott A Nivison, Zachary I. Bell, Rushikesh Kamalapurkar

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

Comments arXiv admin note: substantial text overlap with arXiv:2110.00271

Model-Based Reinforcement Learning Framework of Online Network Resource Allocation

Bahador Bakhshi, Josep Mangues-Bafalluy

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

Comments This is the version of the paper submitted to ICC 2022

Safe Model-Based Reinforcement Learning for Systems with Parametric Uncertainties

S M Nahid Mahmud, Scott A Nivison, Zachary I. Bell, Rushikesh Kamalapurkar

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

Comments This manuscript has been accepted in Frontiers in Robotics and AI. doi: 10.3389/frobt.2021.733104

Safety aware model-based reinforcement learning for optimal control of a class of output-feedback nonlinear systems

S M Nahid Mahmud, Moad Abudia, Scott A Nivison, Zachary I. Bell, Rushikesh Kamalapurkar

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

Comments arXiv admin note: substantial text overlap with arXiv:2007.12666

Adaptive Optics control using Model-Based Reinforcement Learning

Jalo Nousiainen, Chang Rajani, Markus Kasper, Tapio Helin

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

Comments Accepted for publication in Optics Express

Constrained Model-based Reinforcement Learning with Robust Cross-Entropy Method

Zuxin Liu, Hongyi Zhou, Baiming Chen, Sicheng Zhong, Martial Hebert, Ding Zhao

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

Comments 8 pages, 5 figures

Keypoints into the Future: Self-Supervised Correspondence in Model-Based Reinforcement Learning

Lucas Manuelli, Yunzhu Li, Pete Florence, Russ Tedrake

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

Physics Consistency and Latent Dynamics in Spatiotemporal Physics Field Generation

基于混合Mamba-Transformer的时空场生成

Peimian Du, Jiabin Liu, Xiaowei Jin, Wangmeng Zuo, Hui Li

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

AI总结 本文提出基于混合Mamba-Transformer架构的时空物理场生成模型，并通过物理信息微调机制有效减少物理误差。