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Ad hoc teamwork (AHT) is the challenge of designing a robust learner agent that effectively collaborates with unknown teammates without prior coordination mechanisms.
The complexity of decentralized control of markov decision processes
Daniel S Bernstein, Robert Givan, Neil Immerman, and Shlomo Zilberstein · 2002
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Benchmarking multi-agent deep reinforcement learning algorithms in cooperative tasks
Georgios Papoudakis, Filippos Christianos, Lukas Schäfer, and Stefano V. Albrecht · 2006
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Ad hoc autonomous agent teams: Collaboration without pre-coordination
Peter Stone, Gal A. Kaminka, Sarit Kraus, and Jeffrey S. Rosenschein · 2010
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A game-theoretic model and best-response learning method for ad hoc coordination in multiagent systems
Stefano V. Albrecht and Subramanian Ramamoorthy · 2013
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Communicating with unknown teammates
Samuel Barrett, Noa Agmon, Noam Hazon, Sarit Kraus, and Peter Stone · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Belief and truth in hypothesised behaviours
Stefano V. Albrecht, Jacob W. Crandall, and Subramanian Ramamoorthy · 2016
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Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
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Making friends on the fly: Cooperating with new teammates
Samuel Barrett, Avi Rosenfeld, Sarit Kraus, and Peter Stone · 2017
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Population based training of neural networks
Max Jaderberg, Valentin Dalibard, Simon Osindero, Wojciech M. Czarnecki, Jeff Donahue, Ali Razavi, Oriol Vinyals, Tim Green, Iain Dunning, Karen Simonyan, Chrisantha Fernando, and Koray Kavukcuoglu · 2017
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Curiosity-driven exploration by self-supervised prediction
Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, and Trevor Darrell · 2017
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Autonomous agents modelling other agents: A comprehensive survey and open problems
Stefano V. Albrecht and Peter Stone · 2018
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Diversity-driven exploration strategy for deep reinforcement learning
Zhang-Wei Hong, Tzu-Yun Shann, Shih-Yang Su, Yi-Hsiang Chang, Tsu-Jui Fu, and Chun-Yi Lee · 2018
Cited alongside, same era.
Diversity is all you need: Learning skills without a reward function
Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, and Sergey Levine · 2019
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A generalized framework for population based training
Ang Li, Ola Spyra, Sagi Perel, Valentin Dalibard, Max Jaderberg, Chenjie Gu, David Budden, Tim Harley, and Pramod Gupta · 2019
Cited alongside, same era.
Maven: Multi-agent variational exploration
Anuj Mahajan, Tabish Rashid, Mikayel Samvelyan, and Shimon Whiteson · 2019
Cited alongside, same era.
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, Junhyuk Oh, Dan Horgan, Manuel Kroiss, Ivo Danihelka, Aja Huang, Laurent Sifre, Trevor Cai, John P. Agapiou, Max Jaderberg, Alexander S. Vezhnevets, Rémi Leblond, Tobias Pohlen, Valentin Dalibard, David Budden, Yury Sulsky, James Molloy, Tom L. Paine, Caglar Gulcehre, Ziyu Wang, Tobias Pfaff, Yuhuai Wu, Roman Ring, Dani Yogatama, Dario Wünsch, Katrina McKinney, Oliver Smith, Tom Schaul, Timothy Lillicrap, Koray Kavukcuoglu, Demis Hassabis, Chris Apps, and David Silver · 2019
Trajectory diversity for zero-shot coordination
Andrei Lupu, Brandon Cui, Hengyuan Hu, and Jakob Foerster · 2021
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Towards open ad hoc teamwork using graph-based policy learning
Arrasy Rahman, Niklas Höpner, Filippos Christianos, and Stefano V. Albrecht · 2021
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Discovering diverse multi-agent strategic behavior via reward randomization, 2021
Zhenggang Tang, Chao Yu, Boyuan Chen, Huazhe Xu, Xiaolong Wang, Fei Fang, Simon Du, Yu Wang, and Yi Wu · 2021
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Too many cooks: Coordinating multi-agent collaboration through inverse planning
Sarah A. Wu, Rose E. Wang, James A. Evans, Joshua B. Tenenbaum, David C. Parkes, and Max Kleiman-Weiner · 2021
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Learning with generated teammates to achieve type-free ad-hoc teamwork
Dong Xing, Qianhui Liu, Qian Zheng, and Gang Pan · 2021
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Cited alongside, same era.
“other-play” for zero-shot coordination
Hengyuan Hu, Adam Lerer, Alex Peysakhovich, and Jakob Foerster · 2020
Cited alongside, same era.
Effective diversity in population based reinforcement learning
Jack Parker-Holder, Aldo Pacchiano, Krzysztof Choromanski, and Stephen Roberts · 2020
Cited alongside, same era.
Options as responses: Grounding behavioural hierarchies in multi-agent reinforcement learning
Alexander Vezhnevets, Yuhuai Wu, Maria Eckstein, Rémi Leblond, and Joel Z Leibo · 2020
Cited alongside, same era.
Deep reinforcement learning at the edge of the statistical precipice
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C Courville, and Marc Bellemare · 2021
Cited alongside, same era.
Scaling multi-agent reinforcement learning with selective parameter sharing
Filippos Christianos, Georgios Papoudakis, Arrasy Rahman, and Stefano V. Albrecht · 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.
Towards unifying behavioral and response diversity for open-ended learning in zero-sum games
Xiangyu Liu, Hangtian Jia, Ying Wen, Yujing Hu, Yingfeng Chen, Changjie Fan, ZHIPENG HU, and Yaodong Yang · 2021
Cited alongside, same era.
Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms , pp. 321–384
Kaiqing Zhang, Zhuoran Yang, and Tamer Başar · 2021
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Deep interactive bayesian reinforcement learning via meta-learning
Luisa Zintgraf, Sam Devlin, Kamil Ciosek, Shimon Whiteson, and Katja Hofmann · 2021
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Any-play: An intrinsic augmentation for zero-shot coordination
Keane Lucas and Ross E Allen · 2022
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A survey of ad hoc teamwork research
Reuth Mirsky, Ignacio Carlucho, Arrasy Rahman, Elliot Fosong, William Macke, Mohan Sridharan, Peter Stone, and Stefano V Albrecht · 2022
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A general learning framework for open ad hoc teamwork using graph-based policy learning
Arrasy Rahman, Ignacio Carlucho, Niklas Höpner, and Stefano V. Albrecht · 2022
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Learning task embeddings for teamwork adaptation in multi-agent reinforcement learning
Lukas Schäfer, Filippos Christianos, Amos Storkey, and Stefano V. Albrecht · 2022
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Learning zero-shot cooperation with humans, assuming humans are biased
Chao Yu, Jiaxuan Gao, Weiling Liu, Bo Xu, Hao Tang, Jiaqi Yang, Yu Wang, and Yi Ming Wu · 2023
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