世界模型

Feedback Favors the Generalization of Neural ODEs

Jindou Jia, Zihan Yang, Meng Wang, Kexin Guo, Jianfei Yang, Xiang Yu, Lei Guo

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

Comments 27 pages, 23 figures

VideoWorld: Exploring Knowledge Learning from Unlabeled Videos

Zhongwei Ren, Yunchao Wei, Xun Guo, Yao Zhao, Bingyi Kang, Jiashi Feng, Xiaojie Jin

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

Comments Code and models are released at: https://maverickren.github.io/VideoWorld.github.io/

KEEC: Koopman Embedded Equivariant Control

Xiaoyuan Cheng, Yiming Yang, Xiaohang Tang, Wei Jiang, Yukun Hu

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

Reinforcement Learning Enhanced LLMs: A Survey

Shuhe Wang, Shengyu Zhang, Jie Zhang, Runyi Hu, Xiaoya Li, Tianwei Zhang, Jiwei Li, Fei Wu, Guoyin Wang, Eduard Hovy

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

Towards Transparent and Accurate Plasma State Monitoring at JET

Andrin Bürli, Alessandro Pau, Thomas Koller, Olivier Sauter, JET Contributors

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

TD-M(PC)$^2$: Improving Temporal Difference MPC Through Policy Constraint

Haotian Lin, Pengcheng Wang, Jeff Schneider, Guanya Shi

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

Scrutinize What We Ignore: Reining In Task Representation Shift Of Context-Based Offline Meta Reinforcement Learning

Hai Zhang, Boyuan Zheng, Tianying Ji, Jinhang Liu, Anqi Guo, Junqiao Zhao, Lanqing Li

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

Comments Accept at ICLR 2025

Towards General-Purpose Model-Free Reinforcement Learning

Scott Fujimoto, Pierluca D'Oro, Amy Zhang, Yuandong Tian, Michael Rabbat

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

Comments ICLR 2025

Generative AI for Lyapunov Optimization Theory in UAV-based Low-Altitude Economy Networking

Zhang Liu, Dusit Niyato, Jiacheng Wang, Geng Sun, Lianfen Huang, Zhibin Gao, Xianbin Wang

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

Comments 8 pages, 5 figures, magazine paper

Distributional Successor Features Enable Zero-Shot Policy Optimization

Chuning Zhu, Xinqi Wang, Tyler Han, Simon S. Du, Abhishek Gupta

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

Learn A Flexible Exploration Model for Parameterized Action Markov Decision Processes

Zijian Wang, Bin Wang, Mingwen Shao, Hongbo Dou, Boxiang Tao

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

Diminishing Return of Value Expansion Methods

Daniel Palenicek, Michael Lutter, João Carvalho, Daniel Dennert, Faran Ahmad, Jan Peters

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

Comments arXiv admin note: substantial text overlap with arXiv:2303.03955

Provably Efficient Exploration in Reward Machines with Low Regret

Hippolyte Bourel, Anders Jonsson, Odalric-Ambrym Maillard, Chenxiao Ma, Mohammad Sadegh Talebi

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

Comments 35 pages

Reinforcement Learning Ship Autopilot: Sample efficient and Model Predictive Control-based Approach

Yunduan Cui, Shigeki Osaki, Takamitsu Matsubara

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

Comments The 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019)

Rethinking Model-based, Policy-based, and Value-based Reinforcement Learning via the Lens of Representation Complexity

Guhao Feng, Han Zhong

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

Comments NeurIPS 2024; this updated version includes additional theoretical findings and experimental results

Filtered Direct Preference Optimization

Tetsuro Morimura, Mitsuki Sakamoto, Yuu Jinnai, Kenshi Abe, Kaito Ariu

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

Comments EMNLP 2024

Alternators For Sequence Modeling

Mohammad Reza Rezaei, Adji Bousso Dieng

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

Comments A new versatile family of sequence models that can be used for both generative modeling and supervised learning. The codebase will be made available upon publication. This paper is dedicated to Thomas Sankara

Advancing Generalization in PINNs through Latent-Space Representations

Honghui Wang, Yifan Pu, Shiji Song, Gao Huang

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

A Review of Reinforcement Learning in Financial Applications

Yahui Bai, Yuhe Gao, Runzhe Wan, Sheng Zhang, Rui Song

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

$α$-TCVAE: On the relationship between Disentanglement and Diversity

Cristian Meo, Louis Mahon, Anirudh Goyal, Justin Dauwels

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

SPO: Sequential Monte Carlo Policy Optimisation

Matthew V Macfarlane, Edan Toledo, Donal Byrne, Paul Duckworth, Alexandre Laterre

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

Comments Accepted to NeurIPS 2024. 34 pages, 3 main figures

Probabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural Dynamics

Yenho Chen, Noga Mudrik, Kyle A. Johnsen, Sankaraleengam Alagapan, Adam S. Charles, Christopher J. Rozell

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

Comments Accepted to NeurIPS 2024

What model does MuZero learn?

Jinke He, Thomas M. Moerland, Joery A. de Vries, Frans A. Oliehoek

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

Comments ECAI 2024

Bi-Level Motion Imitation for Humanoid Robots

Wenshuai Zhao, Yi Zhao, Joni Pajarinen, Michael Muehlebach

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

Comments CoRL 2024

Model-Based Epistemic Variance of Values for Risk-Aware Policy Optimization

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(abstract)；分类 cs.AI、cs.LG

Comments arXiv admin note: substantial text overlap with arXiv:2302.12526

Towards Scene Graph Anticipation

Rohith Peddi, Saksham Singh, Saurabh, Parag Singla, Vibhav Gogate

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

Comments ECCV 2024, Code: https://github.com/rohithpeddi/SceneSayer

Adaptive Horizon Actor-Critic for Policy Learning in Contact-Rich Differentiable Simulation

Ignat Georgiev, Krishnan Srinivasan, Jie Xu, Eric Heiden, Animesh Garg

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

Comments Website https://adaptive-horizon-actor-critic.github.io/

Offline-Boosted Actor-Critic: Adaptively Blending Optimal Historical Behaviors in Deep Off-Policy RL

Yu Luo, Tianying Ji, Fuchun Sun, Jianwei Zhang, Huazhe Xu, Xianyuan Zhan

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

A Distributional Analogue to the Successor Representation

Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Yunhao Tang, André Barreto, Will Dabney, Marc G. Bellemare, Mark Rowland

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

Comments Accepted to ICML 2024. First two authors contributed equally

Scheduled Curiosity-Deep Dyna-Q: Efficient Exploration for Dialog Policy Learning

Xuecheng Niu, Akinori Ito, Takashi Nose

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

Comments Accepted to IEEE Access

Journal ref IEEE Access, vol. 12, pp. 46940-46952, 2024