世界模型

SJEPA: Learning Elegant Latent Dynamics with Hybrid Symbolic-Neural Predictors

SJEPA：通过混合符号-神经预测器学习简洁的潜在动力学

Yongchao Huang

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

AI总结 SJEPA是一种无重构的JEPA框架，结合符号定律与神经修正学习简洁潜在动力学，在受控摆实验中展现更优性能，揭示了多目标间的可控权衡。

Comments 42 pages

SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning

SINDy-RL：可解释且高效的基于模型的强化学习

Nicholas Zolman, Christian Lagemann, Urban Fasel, J. Nathan Kutz, Steven L. Brunton

机构 * Department of Mechanical Engineering, University of Washington, Seattle, WA 98195, USA（华盛顿大学机械工程系） ； Data Science and Artificial Intelligence Department, The Aerospace Corporation, El Segundo, CA 90245（航空航天公司数据科学与人工智能部） ； Department of Aeronautics, Imperial College, London SW7 2AZ, United Kingdom（帝国理工学院航空系） ； Department of Applied Mathematics, University of Washington, Seattle, WA 98195（华盛顿大学应用数学系） ； Department of Electrical and Computer Engineering, University of Washington, Seattle, WA 98195（华盛顿大学电气与计算机工程系） 纠错

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

AI总结 本文提出SINDy-RL框架，结合SINDy和DRL，实现低数据下高效、可解释的动力学模型和控制策略，通过基准环境和流体控制实验验证其有效性。

Comments For code, see https://github.com/nzolman/sindy-rl. v2 Update: Included Pinball and 3D Airfoil examples. Christian Lagemann added as an author for contributions with the 3D Airfoil code. To appear in Nature Communications

Journal ref Nat. Commun. 16, 10714 (2025)

Safe Model-based Reinforcement Learning with Stability Guarantees

具有稳定性保证的安全模型基于强化学习

Felix Berkenkamp, Matteo Turchetta, Angela P. Schoellig, Andreas Krause

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

AI总结 本文提出一种考虑安全性的强化学习算法，通过Lyapunov稳定性验证理论，利用动态统计模型获得具有证明稳定性的高性能控制策略，并在模拟倒立摆中展示其安全优化神经网络策略的能力。

Comments Proc. of Neural Information Processing Systems (NIPS), 2017

Agnostic System Identification for Model-Based Reinforcement Learning

基于模型的强化学习的不可知系统辨识

Stephane Ross, J. Andrew Bagnell

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

AI总结 针对模型类可能不包含真实系统的不可知情况，提出一种利用无遗憾在线学习算法获得近优策略的迭代方法，并在离散和连续域上验证其有效性。

Comments 8 pages, published in ICML 2012

Geometric Neural Operators via Lie Group-Constrained Latent Dynamics

通过李群约束的潜在动态实现几何神经算子

Jiaquan Zhang, Fachrina Dewi Puspitasari, Songbo Zhang, Yibei Liu, Kuien Liu, Caiyan Qin, Fan Mo, Peng Wang, Yang Yang, Chaoning Zhang

机构 * School of Information and Software Engineering, University of Electronic Science and Technology of China（信息与软件工程学院，电子科学与技术大学） ； Computer Science and Engineering, University of Electronic Science and Technology of China（计算机科学与工程，电子科学与技术大学） ； Institute of Software Chinese Academy of Sciences, Beijing, China（软件研究所，中国科学院） ； School of Robotics and Advanced Manufacture, Harbin Institute of Technology, Shenzhen, China（机器人与先进制造学院，哈尔滨工业大学（深圳）） ； Department of Computer Science, University of Oxford, Oxford, United Kingdom（计算机科学系，牛津大学） 纠错

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

AI总结 本文提出了一种基于李群约束的潜在动态方法，用于改进神经算子的几何诱导偏差，从而提高长期预测的保真度。

Dual Alignment Maximin Optimization for Offline Model-based RL

Chi Zhou, Wang Luo, Haoran Li, Congying Han, Tiande Guo, Zicheng Zhang

机构 * UCAS（中国科学院大学） 纠错

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

Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling

Zhilin Lin, Shiliang Sun

机构 * School of Automation and Intelligent Sensing（自动化与智能感知学院） 纠错

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

Efficient Model-Based Reinforcement Learning Through Optimistic Thompson Sampling

Jasmine Bayrooti, Carl Henrik Ek, Amanda Prorok

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

Comments Appearing in ICLR, 2025

Value-Distributional Model-Based Reinforcement Learning

Carlos E. Luis, Alessandro G. Bottero, Julia Vinogradska, Felix Berkenkamp, Jan Peters

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

Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RL

Jiawei Huang, Niao He, Andreas Krause

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

Comments ICML 2024; 55 Pages

Distributionally Robust Model-based Reinforcement Learning with Large State Spaces

Shyam Sundhar Ramesh, Pier Giuseppe Sessa, Yifan Hu, Andreas Krause, Ilija Bogunovic

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

Journal ref AISTATS 2024

Partial Models for Building Adaptive Model-Based Reinforcement Learning Agents

Safa Alver, Ali Rahimi-Kalahroudi, Doina Precup

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

Comments Published as a conference paper at CoLLAs 2024

Approximating Energy Market Clearing and Bidding With Model-Based Reinforcement Learning

Thomas Wolgast, Astrid Nieße

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

Comments 13 pages

STEERING: Stein Information Directed Exploration for Model-Based Reinforcement Learning

Souradip Chakraborty, Amrit Singh Bedi, Alec Koppel, Mengdi Wang, Furong Huang, Dinesh Manocha

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

Reward-Respecting Subtasks for Model-Based Reinforcement Learning

Richard S. Sutton, Marlos C. Machado, G. Zacharias Holland, David Szepesvari, Finbarr Timbers, Brian Tanner, Adam White

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

Journal ref Artificial Intelligence, first published online September 6, 2023

Value Summation: A Novel Scoring Function for MPC-based Model-based Reinforcement Learning

Mehran Raisi, Amirhossein Noohian, Luc Mccutcheon, Saber Fallah

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

Optimal Exploration for Model-Based RL in Nonlinear Systems

Andrew Wagenmaker, Guanya Shi, Kevin Jamieson

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

Multi-Source 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)；分类 cs.AI、cs.LG

Comments 24 pages, 7 figures, 8 tables. arXiv admin note: text overlap with arXiv:2108.06526

Model Based Reinforcement Learning for Personalized Heparin Dosing

Qinyang He, Yonatan Mintz

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

Comments 48 pages 6 figures

Provably Sample-Efficient RL with Side Information about Latent Dynamics

Yao Liu, Dipendra Misra, Miro Dudík, Robert E. Schapire

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

Comments 35 pages, 4 figures

Free Will Belief as a consequence of Model-based Reinforcement Learning

Erik M. Rehn

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

Neural Marionette: Unsupervised Learning of Motion Skeleton and Latent Dynamics from Volumetric Video

Jinseok Bae, Hojun Jang, Cheol-Hui Min, Hyungun Choi, Young Min Kim

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

Comments 7 pages (main), 10 pages (appendix) and to be appeared in AAAI2022

Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation

Weitong Zhang, Dongruo Zhou, Quanquan Gu

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

Comments 29 pages, 1 figure, 1 table. In NeurIPS 2021

Learning Accurate Long-term Dynamics for Model-based Reinforcement Learning

Nathan O. Lambert, Albert Wilcox, Howard Zhang, Kristofer S. J. Pister, Roberto Calandra

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

Comments 8 pages, +4 pages appendix

Combining Off and On-Policy Training in Model-Based Reinforcement Learning

Alexandre Borges, Arlindo Oliveira

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

Deformable Linear Object Prediction Using Locally Linear Latent Dynamics

Wenbo Zhang, Karl Schmeckpeper, Pratik Chaudhari, Kostas Daniilidis

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

A Sharp Analysis of Model-based Reinforcement Learning with Self-Play

Qinghua Liu, Tiancheng Yu, Yu Bai, Chi Jin

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

Model-based Reinforcement Learning from Signal Temporal Logic Specifications

Parv Kapoor, Anand Balakrishnan, Jyotirmoy V. Deshmukh

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

Comments Submitted to ICRA 2021

Delta Schema Network in Model-based Reinforcement Learning

Andrey Gorodetskiy, Alexandra Shlychkova, Aleksandr I. Panov

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

Comments Published at the AGI 2020 conference

Online Constrained Model-based Reinforcement Learning

Benjamin van Niekerk, Andreas Damianou, Benjamin Rosman

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

Comments Conf. Uncertainty in Artificial Intelligence (UAI). 2017