Fetching the paper…
Reading the bibliography…
Real-world multi-agent tasks usually involve dynamic team composition with the emergence of roles, which should also be a key to efficient cooperation in multi-agent reinforcement learning (MARL).
Algorithm as 136: A k-means clustering algorithm
John A Hartigan and Manchek A Wong · 1979
Earlier work this paper cites.
Qatten: A general framework for cooperative multiagent reinforcement learning
Yaodong Yang, Jianye Hao, Ben Liao, Kun Shao, Guangyong Chen, Wulong Liu, and Hongyao Tang · 2002
Earlier work this paper cites.
Role-assignment in open agent societies
Mehdi Dastani, Virginia Dignum, and Frank Dignum · 2003
Earlier work this paper cites.
Pattern Recognition and Machine Learning , volume 4
Christopher M Bishop · 2006
Earlier work this paper cites.
Automated organization design for multi-agent systems
Mark Sims, Daniel Corkill, and Victor Lesser · 2008
Earlier work this paper cites.
Learning to communicate with deep multi-agent reinforcement learning
Jakob Foerster, Ioannis Alexandros Assael, Nando De Freitas, and Shimon Whiteson · 2016
Earlier work this paper cites.
A Concise Introduction to Decentralized POMDPs , volume 1
Frans A Oliehoek and Christopher Amato · 2016
Earlier work this paper cites.
Learning multiagent communication with backpropagation
Sainbayar Sukhbaatar, Arthur Szlam, and Rob Fergus · 2016
Earlier work this paper cites.
Stabilising experience replay for deep multi-agent reinforcement learning
Jakob Foerster, Nantas Nardelli, Gregory Farquhar, Triantafyllos Afouras, Philip HS Torr, Pushmeet Kohli, and Shimon Whiteson · 2017
Earlier work this paper cites.
Multi-agent reinforcement learning in sequential social dilemmas
Joel Z Leibo, Vinicius Zambaldi, Marc Lanctot, Janusz Marecki, and Thore Graepel · 2017
Earlier work this paper cites.
Multi-agent actor-critic for mixed cooperative-competitive environments
Ryan Lowe, Yi I Wu, Aviv Tamar, Jean Harb, OpenAI Pieter Abbeel, and Igor Mordatch · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Role-based modeling for designing agent behavior in self-organizing multi-agent systems
Kemas M Lhaksmana, Yohei Murakami, and Toru Ishida · 2018
Earlier work this paper cites.
Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
Earlier work this paper cites.
Value-decomposition networks for cooperative multi-agent learning based on team reward
Peter Sunehag, Guy Lever, Audrunas Gruslys, Wojciech Marian Czarnecki, Vinicius Zambaldi, Max Jaderberg, Marc Lanctot, Nicolas Sonnerat, Joel Z Leibo, Karl Tuyls, et al · 2018
Earlier work this paper cites.
Actor-attention-critic for multi-agent reinforcement learning
Shariq Iqbal and Fei Sha · 2019
Earlier work this paper cites.
MAVEN: Multi-agent variational exploration
Anuj Mahajan, Tabish Rashid, Mikayel Samvelyan, and Shimon Whiteson · 2019
Earlier work this paper cites.
The StarCraft multi-agent challenge
Mikayel Samvelyan, Tabish Rashid, Christian Schroeder De Witt, Gregory Farquhar, Nantas Nardelli, Tim GJ Rudner, Chia-Man Hung, Philip HS Torr, Jakob Foerster, and Shimon Whiteson · 2019
Earlier work this paper cites.
QTRAN: Learning to factorize with transformation for cooperative multi-agent reinforcement learning
Kyunghwan Son, Daewoo Kim, Wan Ju Kang, David Earl Hostallero, and Yung Yi · 2019
Earlier work this paper cites.
Grandmaster level in StarCraft II using multi-agent reinforcement learning
Oriol Vinyals, Igor Babuschkin, Wojciech M Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H Choi, Richard Powell, Timo Ewalds, Petko Georgiev, et al · 2019
Cited alongside, same era.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Cited alongside, same era.
Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
Cited alongside, same era.
Google research football: A novel reinforcement learning environment
Karol Kurach, Anton Raichuk, Piotr Stańczyk, Michał Zając, Olivier Bachem, Lasse Espeholt, Carlos Riquelme, Damien Vincent, Marcin Michalski, Olivier Bousquet, et al · 2020
Cited alongside, same era.
CURL: Contrastive unsupervised representations for reinforcement learning
Michael Laskin, Aravind Srinivas, and Pieter Abbeel · 2020
Cited alongside, same era.
Self-organized group for cooperative multi-agent reinforcement learning
Jianzhun Shao, Zhiqiang Lou, Hongchang Zhang, Yuhang Jiang, Shuncheng He, and Xiangyang Ji · 2022
Later among the works it cites.
A contrastive framework for neural text generation
Yixuan Su, Tian Lan, Yan Wang, Dani Yogatama, Lingpeng Kong, and Nigel Collier · 2022
Later among the works it cites.
Multi-agent reinforcement learning is a sequence modeling problem
Muning Wen, Jakub Kuba, Runji Lin, Weinan Zhang, Ying Wen, Jun Wang, and Yaodong Yang · 2022
Later among the works it cites.
LDSA: Learning dynamic subtask assignment in cooperative multi-agent reinforcement learning
Mingyu Yang, Jian Zhao, Xunhan Hu, Wengang Zhou, Jiangcheng Zhu, and Houqiang Li · 2022
Later among the works it cites.
The surprising effectiveness of PPO in cooperative multi-agent games
Chao Yu, Akash Velu, Eugene Vinitsky, Jiaxuan Gao, Yu Wang, Alexandre Bayen, and Yi Wu · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Monotonic value function factorisation for deep multi-agent reinforcement learning
Tabish Rashid, Mikayel Samvelyan, Christian Schroeder De Witt, Gregory Farquhar, Jakob Foerster, and Shimon Whiteson · 2020
Cited alongside, same era.
ROMA: Multi-agent reinforcement learning with emergent roles
Tonghan Wang, Heng Dong, Victor Lesser, and Chongjie Zhang · 2020
Cited alongside, same era.
SMARTS: Scalable multi-agent reinforcement learning training school for autonomous driving
Ming Zhou, Jun Luo, Julian Villella, Yaodong Yang, David Rusu, Jiayu Miao, Weinan Zhang, Montgomery Alban, Iman Fadakar, Zheng Chen, et al · 2020
Cited alongside, same era.
Scaling multi-agent reinforcement learning with selective parameter sharing
Filippos Christianos, Georgios Papoudakis, Muhammad A Rahman, and Stefano V Albrecht · 2021
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2021
Cited alongside, same era.
The emergence of individuality
Jiechuan Jiang and Zongqing Lu · 2021
Cited alongside, same era.
Celebrating diversity in shared multi-agent reinforcement learning
Chenghao Li, Tonghan Wang, Chengjie Wu, Qianchuan Zhao, Jun Yang, and Chongjie Zhang · 2021
Cited alongside, same era.
Robust task representations for offline meta-reinforcement learning via contrastive learning
Haoqi Yuan and Zongqing Lu · 2022
Later among the works it cites.
Multi-agent concentrative coordination with decentralized task representation
Lei Yuan, Chenghe Wang, Jianhao Wang, Fuxiang Zhang, Feng Chen, Cong Guan, Zongzhang Zhang, Chongjie Zhang, and Yang Yu · 2022
Later among the works it cites.
LINDA: Multi-agent local information decomposition for awareness of teammates
Jiahan Cao, Lei Yuan, Jianhao Wang, Shaowei Zhang, Chongjie Zhang, Yang Yu, and De-Chuan Zhan · 2023
Closest in time.
Contrastive identity-aware learning for multi-agent value decomposition
Shunyu Liu, Yihe Zhou, Jie Song, Tongya Zheng, Kaixuan Chen, Tongtian Zhu, Zunlei Feng, and Mingli Song · 2023
Closest in time.
Attention-based recurrence for multi-agent reinforcement learning under stochastic partial observability
Thomy Phan, Fabian Ritz, Philipp Altmann, Maximilian Zorn, Jonas Nüßlein, Michael Kölle, Thomas Gabor, and Claudia Linnhoff-Popien · 2023
Closest in time.
Complementary attention for multi-agent reinforcement learning
Jianzhun Shao, Hongchang Zhang, Yun Qu, Chang Liu, Shuncheng He, Yuhang Jiang, and Xiangyang Ji · 2023
Closest in time.
MA2CL: Masked attentive contrastive learning for multi-agent reinforcement learning
Haolin Song, Mingxiao Feng, Wengang Zhou, and Houqiang Li · 2023
Closest in time.
More centralized training, still decentralized execution: Multi-agent conditional policy factorization
Jiangxing Wang, Deheng Ye, and Zongqing Lu · 2023
Closest in time.
Dynamic role discovery and assignment in multi-agent task decomposition
Yu Xia, Junwu Zhu, and Liucun Zhu · 2023
Closest in time.
Asynchronous multi-agent reinforcement learning for efficient real-time multi-robot cooperative exploration
Chao Yu, Xinyi Yang, Jiaxuan Gao, Jiayu Chen, Yunfei Li, Jijia Liu, Yunfei Xiang, Ruixin Huang, Huazhong Yang, Yi Wu, and Yu Wang · 2023
Closest in time.
Automatic grouping for efficient cooperative multi-agent reinforcement learning
Yifan Zang, Jinmin He, Kai Li, Haobo Fu, Qiang Fu, Junliang Xing, and Jian Cheng · 2023
Closest in time.
Effective and stable role-based multi-agent collaboration by structural information principles
Xianghua Zeng, Hao Peng, and Angsheng Li · 2023
Closest in time.
Dynamic belief for decentralized multi-agent cooperative learning
Yunpeng Zhai, Peixi Peng, Chen Su, and Yonghong Tian · 2023
Closest in time.