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World models have recently attracted growing interest in Multi-Agent Reinforcement Learning (MARL) due to their ability to improve sample efficiency for policy learning.
Kyunghwan Son, Daewoo Kim, Wan Ju Kang, David Earl Hostallero, and Yung Yi · 1905
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Multiagent rollout algorithms and reinforcement learning, 2020
Dimitri Bertsekas · 1910
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Estimation of non-normalized statistical models by score matching
Aapo Hyvärinen and Peter Dayan · 2005
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Qplex: Duplex dueling multi-agent q-learning, 2021
Jianhao Wang, Zhizhou Ren, Terry Liu, Yang Yu, and Chongjie Zhang · 2008
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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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A concise introduction to decentralized POMDPs , volume 1
Frans A Oliehoek, Christopher Amato, et al · 2016
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Gaussian error linear units (gelus)
Dan Hendrycks and Kevin Gimpel · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Neural discrete representation learning
Aaron Van Den Oord, Oriol Vinyals, et al · 2017
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Value-decomposition networks for cooperative multi-agent learning, 2017
Peter Sunehag, Guy Lever, Audrunas Gruslys, Wojciech Marian Czarnecki, Vinicius Zambaldi, Max Jaderberg, Marc Lanctot, Nicolas Sonnerat, Joel Z. Leibo, Karl Tuyls, and Thore Graepel · 2017
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Qmix: Monotonic value function factorisation for deep multi-agent reinforcement learning, 2018
Tabish Rashid, Mikayel Samvelyan, Christian Schroeder de Witt, Gregory Farquhar, Jakob Foerster, and Shimon Whiteson · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
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Dream to control: Learning behaviors by latent imagination
Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi · 2020
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Mastering atari, go, chess and shogi by planning with a learned model
Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, et al · 2020
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A very condensed survey and critique of multiagent deep reinforcement learning
Pablo Hernandez-Leal, Bilal Kartal, and Matthew E. Taylor · 2020
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Deep reinforcement learning for multiagent systems: A review of challenges, solutions, and applications
Thanh Thi Nguyen, Ngoc Duy Nguyen, and Saeid Nahavandi · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
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mingpt: A minimal pytorch re-implementation of the openai gpt (generative pretrained transformer) training
Andrej Karpathy · 2020
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Model based reinforcement learning for atari
Łukasz Kaiser, Mohammad Babaeizadeh, Piotr Miłos, Błażej Osiński, Roy H Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, Afroz Mohiuddin, Ryan Sepassi, George Tucker, and Henryk Michalewski · 2020
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Mastering atari with discrete world models
Danijar Hafner, Timothy P Lillicrap, Mohammad Norouzi, and Jimmy Ba · 2021
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Mastering atari games with limited data
Weirui Ye, Shaohuai Liu, Thanard Kurutach, Pieter Abbeel, and Yang Gao · 2021
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Facmac: Factored multi-agent centralised policy gradients
Bei Peng, Tabish Rashid, Christian Schroeder de Witt, Pierre-Alexandre Kamienny, Philip Torr, Wendelin Böhmer, and Shimon Whiteson · 2021
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Temporal difference learning for model predictive control
Jianing Ye, Chenghao Li, Jianhao Wang, and Chongjie Zhang · 2023
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Zifan Wu, Chao Yu, Chen Chen, Jianye Hao, and Hankz Hankui Zhuo · 2023
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TD-MPC2: Scalable, robust world models for continuous control
Nicklas Hansen, Hao Su, and Xiaolong Wang · 2024
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Yang Zhang, Chenjia Bai, Bin Zhao, Junchi Yan, Xiu Li, and Xuelong Li · 2024
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Diffusion for world modeling: Visual details matter in atari
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Nicklas A Hansen, Hao Su, and Xiaolong Wang · 2022
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Scalable multi-agent model-based reinforcement learning
Vladimir Egorov and Alexei Shpilman · 2022
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Elucidating the design space of diffusion-based generative models
Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine · 2022
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Towards human-level bimanual dexterous manipulation with reinforcement learning
Yuanpei Chen, Yaodong Yang, Tianhao Wu, Shengjie Wang, Xidong Feng, Jiechuan Jiang, Zongqing Lu, Stephen Marcus McAleer, Hao Dong, and Song-Chun Zhu · 2022
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The surprising effectiveness of ppo in cooperative, multi-agent games, 2022
Chao Yu, Akash Velu, Eugene Vinitsky, Jiaxuan Gao, Yu Wang, Alexandre Bayen, and Yi Wu · 2022
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Is conditional generative modeling all you need for decision-making?
Anurag Ajay, Yilun Du, Abhi Gupta, Joshua Tenenbaum, Tommi Jaakkola, and Pulkit Agrawal · 2022
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Planning with diffusion for flexible behavior synthesis
Michael Janner, Yilun Du, Joshua B. Tenenbaum, and Sergey Levine · 2022
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Madiff: Offline multi-agent learning with diffusion models
Zhengbang Zhu, Minghuan Liu, Liyuan Mao, Bingyi Kang, Minkai Xu, Yong Yu, Stefano Ermon, and Weinan Zhang · 2024
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Finite scalar quantization: VQ-VAE made simple
Fabian Mentzer, David Minnen, Eirikur Agustsson, and Michael Tschannen · 2024
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Efficient planning with latent diffusion
Wenhao Li · 2024
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Crossway diffusion: Improving diffusion-based visuomotor policy via self-supervised learning, 2024
Xiang Li, Varun Belagali, Jinghuan Shang, and Michael S. Ryoo · 2024
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π \pi 0: A vision-language-action flow model for general robot control, 2024
Kevin Black, Noah Brown, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, Lachy Groom, Karol Hausman, Brian Ichter, et al · 2024
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World models via policy-guided trajectory diffusion, 2024
Marc Rigter, Jun Yamada, and Ingmar Posner · 2024
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Robodreamer: Learning compositional world models for robot imagination
Siyuan Zhou, Yilun Du, Jiaben Chen, YANDONG LI, Dit-Yan Yeung, and Chuang Gan · 2024
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Counterfactual multi-agent policy gradients, 2024
Jakob Foerster, Gregory Farquhar, Triantafyllos Afouras, Nantas Nardelli, and Shimon Whiteson · 2024
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Mastering diverse domains through world models, 2024
Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, and Timothy Lillicrap · 2024
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Heterogeneous-agent reinforcement learning
Yifan Zhong, Jakub Grudzien Kuba, Xidong Feng, Siyi Hu, Jiaming Ji, and Yaodong Yang · 2024
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Diffusion models are real-time game engines
Dani Valevski, Yaniv Leviathan, Moab Arar, and Shlomi Fruchter · 2025
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Efficient multi-agent offline coordination via diffusion-based trajectory stitching
Lei Yuan, Yuqi Bian, Lihe Li, Ziqian Zhang, Cong Guan, and Yang Yu · 2025
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Diffusion actor-critic: Formulating constrained policy iteration as diffusion noise regression for offline reinforcement learning
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Soft diffusion actor-critic: Efficient online reinforcement learning for diffusion policy, 2025
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Dof: A diffusion factorization framework for offline multi-agent reinforcement learning
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