世界模型

State-Wise Safe Reinforcement Learning With Pixel Observations

Simon Sinong Zhan, Yixuan Wang, Qingyuan Wu, Ruochen Jiao, Chao Huang, Qi Zhu

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

Comments 10 pages, 5 figures

Look Beneath the Surface: Exploiting Fundamental Symmetry for Sample-Efficient Offline RL

Peng Cheng, Xianyuan Zhan, Zhihao Wu, Wenjia Zhang, Shoucheng Song, Han Wang, Youfang Lin, Li Jiang

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

Comments Accepted in NeurIPS 2023; The first two authors contributed equally

Benchmark Generation Framework with Customizable Distortions for Image Classifier Robustness

Soumyendu Sarkar, Ashwin Ramesh Babu, Sajad Mousavi, Zachariah Carmichael, Vineet Gundecha, Sahand Ghorbanpour, Ricardo Luna, Gutierrez Antonio Guillen, Avisek Naug

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 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

How to Learn and Generalize From Three Minutes of Data: Physics-Constrained and Uncertainty-Aware Neural Stochastic Differential Equations

Franck Djeumou, Cyrus Neary, Ufuk Topcu

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

Comments Final submission to CoRL 2023

Probabilistic Reach-Avoid for Bayesian Neural Networks

Matthew Wicker, Luca Laurenti, Andrea Patane, Nicola Paoletti, Alessandro Abate, Marta Kwiatkowska

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

Comments 47 pages, 10 figures. arXiv admin note: text overlap with arXiv:2105.10134

Signal Temporal Logic Neural Predictive Control

Yue Meng, Chuchu Fan

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

Comments Accepted by IEEE Robotics and Automation Letters (RA-L) and ICRA2024

Deep Generative Models for Decision-Making and Control

Michael Janner

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

Comments UC Berkeley PhD thesis; supersedes arXiv:2010.14496, arXiv:2106.02039, and arXiv:2205.09991

Relate to Predict: Towards Task-Independent Knowledge Representations for Reinforcement Learning

Thomas Schnürer, Malte Probst, Horst-Michael Gross

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

Comments submitted to IJCNN 2022

Contrastive Value Learning: Implicit Models for Simple Offline RL

Bogdan Mazoure, Benjamin Eysenbach, Ofir Nachum, Jonathan Tompson

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

Comments Deep Reinforcement Learning Workshop, NeurIPS 2022

Safe Policy Improvement in Constrained Markov Decision Processes

Luigi Berducci, Radu Grosu

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

Comments Accepted for presentation at the International Symposium on Leveraging Applications of Formal Methods (ISoLA, 2022)

Journal ref LNCS 13701 (2022) 360-381;

Temporal Difference Learning for Model Predictive Control

Nicklas Hansen, Xiaolong Wang, Hao Su

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

Comments Code and videos: https://nicklashansen.github.io/td-mpc

Explicit Explore, Exploit, or Escape ($E^4$): near-optimal safety-constrained reinforcement learning in polynomial time

David M. Bossens, Nicholas Bishop

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

Comments Accepted at Machine Learning

Safe Reinforcement Learning Using Robust Control Barrier Functions

Yousef Emam, Gennaro Notomista, Paul Glotfelter, Zsolt Kira, Magnus Egerstedt

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

Comments Submitted to IEEE Robotics and Automation Letters (RA-L)

Learning Task Informed Abstractions

Xiang Fu, Ge Yang, Pulkit Agrawal, Tommi Jaakkola

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

Comments 8 pages, 12 figures

NewtonianVAE: Proportional Control and Goal Identification from Pixels via Physical Latent Spaces

Miguel Jaques, Michael Burke, Timothy Hospedales

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

Minimax Model Learning

Cameron Voloshin, Nan Jiang, Yisong Yue

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

Journal ref PMLR, Volume 130, 2021

Visualizing MuZero Models

Joery A. de Vries, Ken S. Voskuil, Thomas M. Moerland, Aske Plaat

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

Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes

Mengdi Xu, Wenhao Ding, Jiacheng Zhu, Zuxin Liu, Baiming Chen, Ding Zhao

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

Comments 16 pages, 6 figures

Learning Latent Representations to Influence Multi-Agent Interaction

Annie Xie, Dylan P. Losey, Ryan Tolsma, Chelsea Finn, Dorsa Sadigh

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

Comments Conference on Robot Learning (CoRL) 2020. Supplementary website at https://sites.google.com/view/latent-strategies/

Model-based Policy Optimization with Unsupervised Model Adaptation

Jian Shen, Han Zhao, Weinan Zhang, Yong Yu

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

Comments Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS 2020)

Bidirectional Model-based Policy Optimization

Hang Lai, Jian Shen, Weinan Zhang, Yong Yu

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

Comments Accepted at ICML2020

Deep Learning Tubes for Tube MPC

David D. Fan, Ali-akbar Agha-mohammadi, Evangelos A. Theodorou

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

Comments RSS 2020 Camera Ready Version

Learning to Correspond Dynamical Systems

Nam Hee Kim, Zhaoming Xie, Michiel van de Panne

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

Domain Knowledge Integration By Gradient Matching For Sample-Efficient Reinforcement Learning

Parth Chadha

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

Model-based Multi-Agent Reinforcement Learning with Cooperative Prioritized Sweeping

Eugenio Bargiacchi, Timothy Verstraeten, Diederik M. Roijers, Ann Nowé

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

Augmented Random Search for Quadcopter Control: An alternative to Reinforcement Learning

Ashutosh Kumar Tiwari, Sandeep Varma Nadimpalli

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

Comments 10 pages. 11 figures, Published in International Journal of Information Technology and Computer Science(IJITCS), http://www.mecs-press.org/ijitcs

Journal ref IJITCS Vol. 11, No. 11, Nov. 2019 , Page Range. 24-33

Robust Model-free Reinforcement Learning with Multi-objective Bayesian Optimization

Matteo Turchetta, Andreas Krause, Sebastian Trimpe

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

Comments Submitted to IEEE Conference on Robotics and Automation 2020 (ICRA)

Model-based Lookahead Reinforcement Learning

Zhang-Wei Hong, Joni Pajarinen, Jan Peters

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

Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning

Anusha Nagabandi, Ignasi Clavera, Simin Liu, Ronald S. Fearing, Pieter Abbeel, Sergey Levine, Chelsea Finn

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

Comments First 2 authors contributed equally. Website: https://sites.google.com/berkeley.edu/metaadaptivecontrol

PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos

Paavo Parmas, Carl Edward Rasmussen, Jan Peters, Kenji Doya

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

Comments ICML 2018