世界模型

Deformable Elasto-Plastic Object Shaping using an Elastic Hand and Model-Based Reinforcement Learning

Carolyn Matl, Ruzena Bajcsy

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

Comments 9 pages, 6 figures, To be published in Proc. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Learning Latent Dynamics for Partially-Observed Chaotic Systems

Said Ouala, Duong Nguyen, Lucas Drumetz, Bertrand Chapron, Ananda Pascual, Fabrice Collard, Lucile Gaultier, Ronan Fablet

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

Adaptive Discretization for Model-Based Reinforcement Learning

Sean R. Sinclair, Tianyu Wang, Gauri Jain, Siddhartha Banerjee, Christina Lee Yu

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

Comments 50 pages, 7 figures

Model-Based Reinforcement Learning with a Generative Model is Minimax Optimal

Alekh Agarwal, Sham Kakade, Lin F. Yang

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

Learning Powerful Policies by Using Consistent Dynamics Model

Shagun Sodhani, Anirudh Goyal, Tristan Deleu, Yoshua Bengio, Sergey Levine, Jian Tang

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

Comments Accpted at RLDM 2019

Automatic Machine Learning by Pipeline Synthesis using Model-Based Reinforcement Learning and a Grammar

Iddo Drori, Yamuna Krishnamurthy, Raoni Lourenco, Remi Rampin, Kyunghyun Cho, Claudio Silva, Juliana Freire

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

Comments ICML Workshop on Automated Machine Learning

A Model-Based Reinforcement Learning Approach for a Rare Disease Diagnostic Task

Rémi Besson, Erwan Le Pennec, Stéphanie Allassonnière, Julien Stirnemann, Emmanuel Spaggiari, Antoine Neuraz

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

Comments 24 pages

Multi-task learning with deep model based reinforcement learning

Asier Mujika

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

Policy Error Bounds for Model-Based Reinforcement Learning with Factored Linear Models

Bernardo Ávila Pires, Csaba Szepesvári

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

Comments 30 pages. Corrected typos. Appears in JMLR Workshop and Conference Proceedings 49: Proceedings of the 29th Annual Conference on Learning Theory (COLT 2016)

Journal ref JMLR W&CP 49: COLT 2016 Proceedings (2016) 1-31

Why and How Auxiliary Tasks Improve JEPA Representations

Jiacan Yu, Siyi Chen, Mingrui Liu, Nono Horiuchi, Vladimir Braverman, Zicheng Xu, Dan Haramati, Randall Balestriero

机构 * Johns Hopkins University（约翰霍普金斯大学） ； Northwestern University（西北大学） ； University of Rochester（罗切斯特大学） ； Brown University（布朗大学） 纠错

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

IQL-TD-MPC: Implicit Q-Learning for Hierarchical Model Predictive Control

Rohan Chitnis, Yingchen Xu, Bobak Hashemi, Lucas Lehnert, Urun Dogan, Zheqing Zhu, Olivier Delalleau

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

Journal ref Short version published at ICRA 2024 (https://tinyurl.com/icra24-iqltdmpc)

Optimistic MLE -- A Generic Model-based Algorithm for Partially Observable Sequential Decision Making

Qinghua Liu, Praneeth Netrapalli, Csaba Szepesvári, Chi Jin

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

Learning Parsimonious Dynamics for Generalization in Reinforcement Learning

Tankred Saanum, Eric Schulz

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

Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees

Yuping Luo, Huazhe Xu, Yuanzhi Li, Yuandong Tian, Trevor Darrell, Tengyu Ma

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

Comments Added important notes that the conditions of Theorem 3.1 cannot simultaneously hold for most models

The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning

Harm van Seijen, Hadi Nekoei, Evan Racah, Sarath Chandar

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

Comments NeurIPS 2020, code: https://github.com/chandar-lab/LoCA

Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning

Younggyo Seo, Kimin Lee, Ignasi Clavera, Thanard Kurutach, Jinwoo Shin, Pieter Abbeel

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

Comments Accepted in NeurIPS2020. First two authors contributed equally, website: https://sites.google.com/view/trajectory-mcl code: https://github.com/younggyoseo/trajectory_mcl

Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search

Arthur Guez, David Silver, Peter Dayan

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

Comments 14 pages, 7 figures, includes supplementary material. Advances in Neural Information Processing Systems (NIPS) 2012

Journal ref (2012) Advances in Neural Information Processing Systems 25, pages 1034-1042

LNODE: latent dynamics reveal the shared spatiotemporal structure of amyloid-$β$ progression

LNODE：潜变量揭示阿尔茨海默病β淀粉样蛋白进展的共享时空结构

Zheyu Wen, George Biros

专题命中 模型式强化学习 ：latent dynamics(title)

AI总结 LNODE模型通过PET影像校准，揭示阿尔茨海默病β淀粉样蛋白进展的时空结构，具备融合、定量分析和解释能力，展现高参数可识别性和稳定性。

Comments 38 pages, 13 figures

Optimal Control of Probabilistic Dynamics Models via Mean Hamiltonian Minimization

David Leeftink, Çağatay Yıldız, Steffen Ridderbusch, Max Hinne, Marcel van Gerven

机构 * Department of Machine Learning and Neural Computing, Donders Institute for Brain, Cognition and Behaviour, Radboud University（机器学习与神经计算系，多纳尔斯脑认知行为研究所，拉德堡德大学） ； Cluster of Excellence Machine Learning, University of Tübingen（卓越机器学习集群，图宾根大学） ； Department of Engineering, Control Group, University of Oxford（工程系，控制组，牛津大学） 纠错

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

Comments 8 pages, 4 figures, 2 tables

Probabilistically safe and efficient model-based reinforcement learning

Filippo Airaldi, Bart De Schutter, Azita Dabiri

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

Comments 8 pages, 4 figures, accepted to 2025 CDC

Learning safety in model-based Reinforcement Learning using MPC and Gaussian Processes

Filippo Airaldi, Bart De Schutter, Azita Dabiri

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

Comments 8 pages, 3 figures, submitted to IFAC World Congress 2023 (implemented reviews)

Journal ref IFAC-PapersOnLine, 56 (2), 2023, 5759-5764

Bimanual Deformable Bag Manipulation Using a Structure-of-Interest Based Neural Dynamics Model

Peng Zhou, Pai Zheng, Jiaming Qi, Chenxi Li, Samantha Lee, Chenguang Yang, David Navarro-Alarcon, Jia Pan

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

Reinforcement Twinning: from digital twins to model-based reinforcement learning

Lorenzo Schena, Pedro Marques, Romain Poletti, Samuel Ahizi, Jan Van den Berghe, Miguel A. Mendez

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

Comments submitted Journal of Computational Science

On learning latent dynamics of the AUG plasma state

A. Kit, A. E. Järvinen, Y. R. J. Poels, S. Wiesen, V. Menkovski, R. Fischer, M. Dunne, ASDEX-Upgrade Team

专题命中 模型式强化学习 ：latent dynamics(title)

Comments 9 pages, 11 figures, 4th International Conference on Data-Driven Plasma Sciences

Journal ref Phys. Plasmas 31, 032504 (2024)

Efficient Preference-Based Reinforcement Learning Using Learned Dynamics Models

Yi Liu, Gaurav Datta, Ellen Novoseller, Daniel S. Brown

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

Comments In proceedings of the 2023 IEEE International Conference on Robotics and Automation (ICRA 2023)

Dynamic Programming-based Approximate Optimal Control for Model-Based Reinforcement Learning

Prakash Mallick, Zhiyong Chen

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

Comments 12 Pages, 6 figures. arXiv admin note: text overlap with arXiv:2010.00207

Reward-Consistent Dynamics Models are Strongly Generalizable for Offline Reinforcement Learning

Fan-Ming Luo, Tian Xu, Xingchen Cao, Yang Yu

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

Neural-network learning of SPOD latent dynamics

Andrea Lario, Romit Maulik, Oliver T. Schmidt, Gianluigi Rozza, Gianmarco Mengaldo

专题命中 模型式强化学习 ：latent dynamics(title)

Comments 29 pages, 18 figures, 6 tables

TartanDrive: A Large-Scale Dataset for Learning Off-Road Dynamics Models

Samuel Triest, Matthew Sivaprakasam, Sean J. Wang, Wenshan Wang, Aaron M. Johnson, Sebastian Scherer

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

Investigating Compounding Prediction Errors in Learned Dynamics Models

Nathan Lambert, Kristofer Pister, Roberto Calandra

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

Comments 25 pages, 19 figures