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Building agents with adaptive behavior in cooperative tasks stands as a paramount goal in the realm of multi-agent systems.
Language Models are Few-Shot Learners
Brown, T.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J. D.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; et al. 2020 · 1901
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TD-Gammon, a self-teaching backgammon program, achieves master-level play
Tesauro, G. 1994 · 1994
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An Overview of Multi-Agent Reinforcement Learning from Game Theoretical Perspective
Yang, Y.; and Wang, J. 2021 · 2011
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Population Based Training of Neural Networks
Jaderberg, M.; Dalibard, V.; Osindero, S.; Czarnecki, W. M.; Donahue, J.; Razavi, A.; Vinyals, O.; Green, T.; Dunning, I.; Simonyan, K.; Fernando, C.; and Kavukcuoglu, K. 2017 · 2017
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QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
Rashid, T.; Samvelyan, M.; Schroeder, C.; Farquhar, G.; Foerster, J.; and Whiteson, S. 2018 · 2018
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On the Utility of Learning about Humans for Human-AI Coordination
Carroll, M.; Shah, R.; Ho, M. K.; Griffiths, T.; Seshia, S.; Abbeel, P.; and Dragan, A. 2019 · 2019
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Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning
Hu, H.; and Foerster, J. N. 2020 · 2020
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Grounding Language to Entities and Dynamics for Generalization in Reinforcement Learning
Hanjie, A. W.; Zhong, V. Y.; and Narasimhan, K. 2021 · 2021
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Trajectory Diversity for Zero-Shot Coordination
Lupu, A.; Cui, B.; Hu, H.; and Foerster, J. 2021 · 2021
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Collaborating with Humans without Human Data
Strouse, D.; McKee, K.; Botvinick, M.; Hughes, E.; and Everett, R. 2021 · 2021
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ToM2C: Target-oriented Multi-agent Communication and Cooperation with Theory of Mind
Wang, Y.; Zhong, F.; Xu, J.; and Wang, Y. 2021 · 2021
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Too Many Cooks: Bayesian Inference for Coordinating Multi-Agent Collaboration
Wu, S. A.; Wang, R. E.; Evans, J. A.; Tenenbaum, J. B.; Parkes, D. C.; and Kleiman-Weiner, M. 2021 · 2021
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Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms
Zhang, K.; Yang, Z.; and Başar, T. 2021 · 2021
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Do As I Can, Not As I Say: Grounding Language in Robotic Affordances
Ahn, M.; Brohan, A.; Brown, N.; Chebotar, Y.; Cortes, O.; David, B.; Finn, C.; Fu, C.; Gopalakrishnan, K.; Hausman, K.; Herzog, A.; Ho, D.; Hsu, J.; Ibarz, J.; Ichter, B.; Irpan, A.; Jang, E.; Ruano, R. J.; Jeffrey, K.; Jesmonth, S.; Joshi, N. J.; Julian, R.; Kalashnikov, D.; Kuang, Y.; Lee, K.-H.; Levine, S.; Lu, Y.; Luu, L.; Parada, C.; Pastor, P.; Quiambao, J.; Rao, K.; Rettinghouse, J.; Reyes, D.; Sermanet, P.; Sievers, N.; Tan, C.; Toshev, A.; Vanhoucke, V.; Xia, F.; Xiao, T.; Xu, P.; Xu, S.; Yan, M.; and Zeng, A. 2022 · 2022
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MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge
Fan, L.; Wang, G.; Jiang, Y.; Mandlekar, A.; Yang, Y.; Zhu, H.; Tang, A.; Huang, D.-A.; Zhu, Y.; and Anandkumar, A. 2022 · 2022
Cited alongside, same era.
Multi-Agent Deep Reinforcement Learning: A survey
Gronauer, S.; and Diepold, K. 2022 · 2022
Cited alongside, same era.
Large Language Models are Zero-Shot Reasoners
Kojima, T.; Gu, S. S.; Reid, M.; Matsuo, Y.; and Iwasawa, Y. 2022 · 2022
Cited alongside, same era.
Any-Play: An Intrinsic Augmentation for Zero-Shot Coordination
Lucas, K.; and Allen, R. E. 2022 · 2022
Cited alongside, same era.
Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC Tasks
Meng, L.; Wen, M.; Yang, Y.; Le, C.; Li, X.; Zhang, W.; Wen, Y.; Zhang, H.; Wang, J.; and Xu, B. 2022 · 2022
Cited alongside, same era.
Towards Reasoning in Large Language Models: A Survey
Huang, J.; and Chang, K. C.-C. 2023 · 2023
Closest in time.
Code as Policies: Language Model Programs for Embodied Control
Liang, J.; Huang, W.; Xia, F.; Xu, P.; Hausman, K.; Ichter, B.; Florence, P.; and Zeng, A. 2023 · 2023
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Augmented Language Models: a Survey
Mialon, G.; Dessì, R.; Lomeli, M.; Nalmpantis, C.; Pasunuru, R.; Raileanu, R.; Rozière, B.; Schick, T.; Dwivedi-Yu, J.; Celikyilmaz, A.; Grave, E.; LeCun, Y.; and Scialom, T. 2023 · 2023
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REFINER: Reasoning Feedback on Intermediate Representations
Paul, D.; Ismayilzada, M.; Peyrard, M.; Borges, B.; Bosselut, A.; West, R.; and Faltings, B. 2023 · 2023
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Reflexion: Language Agents with Verbal Reinforcement Learning
Shinn, N.; Cassano, F.; Gopinath, A.; Narasimhan, K. R.; and Yao, S. 2023 · 2023
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Ouyang, L.; Wu, J.; Jiang, X.; Almeida, D.; Wainwright, C.; Mishkin, P.; Zhang, C.; Agarwal, S.; Slama, K.; Ray, A.; Schulman, J.; Hilton, J.; Kelton, F.; Miller, L.; Simens, M.; Askell, A.; Welinder, P.; Christiano, P. F.; Leike, J.; and Lowe, R. 2022 · 2022
Cited alongside, same era.
Chain of Thought Prompting Elicits Reasoning in Large Language Models
Wei, J.; Wang, X.; Schuurmans, D.; Bosma, M.; brian ichter; Xia, F.; Chi, E. H.; Le, Q. V.; and Zhou, D. 2022 · 2022
Cited alongside, same era.
Multi-Agent Reinforcement Learning is a Sequence Modeling Problem
Wen, M.; Kuba, J.; Lin, R.; Zhang, W.; Wen, Y.; Wang, J.; and Yang, Y. 2022 · 2022
Cited alongside, same era.
The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games
Yu, C.; Velu, A.; Vinitsky, E.; Gao, J.; Wang, Y.; Bayen, A.; and Wu, Y. 2022 · 2022
Cited alongside, same era.
Sparks of Artificial General Intelligence: Early Experiments with GPT-4
Bubeck, S.; Chandrasekaran, V.; Eldan, R.; Gehrke, J.; Horvitz, E.; Kamar, E.; Lee, P.; Lee, Y. T.; Li, Y.; Lundberg, S.; Nori, H.; Palangi, H.; Ribeiro, M. T.; and Zhang, Y. 2023 · 2023
Cited alongside, same era.
Entity Divider with Language Grounding in Multi-Agent Reinforcement Learning
Ding, Z.; Zhang, W.; Yue, J.; Wang, X.; Huang, T.; and Lu, Z. 2023 · 2023
Cited alongside, same era.
Guiding Pretraining in Reinforcement Learning with Large Language Models
Du, Y.; Watkins, O.; Wang, Z.; Colas, C.; Darrell, T.; Abbeel, P.; Gupta, A.; and Andreas, J. 2023 · 2023
Cited alongside, same era.
Closest in time.
ProgPrompt: Generating Situated Robot Task Plans using Large Language Models
Singh, I.; Blukis, V.; Mousavian, A.; Goyal, A.; Xu, D.; Tremblay, J.; Fox, D.; Thomason, J.; and Garg, A. 2023 · 2023
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Generating Sequences by Learning to Self-Correct
Welleck, S.; Lu, X.; West, P.; Brahman, F.; Shen, T.; Khashabi, D.; and Choi, Y. 2023 · 2023
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ReAct: Synergizing Reasoning and Acting in Language Models
Yao, S.; Zhao, J.; Yu, D.; Du, N.; Shafran, I.; Narasimhan, K. R.; and Cao, Y. 2023 · 2023
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Building Cooperative Embodied Agents Modularly with Large Language Models
Zhang, H.; Du, W.; Shan, J.; Zhou, Q.; Du, Y.; Tenenbaum, J. B.; Shu, T.; and Gan, C. 2023 · 2023
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Maximum Entropy Population Based Training for Zero-Shot Human-AI Coordination
Zhao, R.; Song, J.; Yuan, Y.; Hu, H.; Gao, Y.; Wu, Y.; Sun, Z.; and Yang, W. 2023 · 2023
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Heterogeneous-Agent Reinforcement Learning
Zhong, Y.; Kuba, J. G.; Feng, X.; Hu, S.; Ji, J.; and Yang, Y. 2023 · 2023
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Least-to-Most Prompting Enables Complex Reasoning in Large Language Models
Zhou, D.; Schärli, N.; Hou, L.; Wei, J.; Scales, N.; Wang, X.; Schuurmans, D.; Cui, C.; Bousquet, O.; Le, Q. V.; and Chi, E. H. 2023 · 2023
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Tackling Cooperative Incompatibility for Zero-Shot Human-AI Coordination
Li, Y.; Zhang, S.; Sun, J.; Zhang, W.; Du, Y.; Wen, Y.; Wang, X.; and Pan, W. 2024 · 2024
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