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Large Language Models (LLMs) have emerged as integral tools for reasoning, planning, and decision-making, drawing upon their extensive world knowledge and proficiency in language-related tasks.
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Multi-agent actor-critic for mixed cooperative-competitive environments
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Virtualhome: Simulating household activities via programs
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Social influence as intrinsic motivation for multi-agent deep reinforcement learning
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The starcraft multi-agent challenge
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Grandmaster level in StarCraft II using multi-agent reinforcement learning
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Tianyu Gao, Adam Fisch, and Danqi Chen · 2020
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Autoprompt: Eliciting knowledge from language models with automatically generated prompts
Taylor Shin, Yasaman Razeghi, Robert L Logan IV, Eric Wallace, and Sameer Singh · 2020
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Multi-agent graph reinforcement learning for connected automated driving
Jiawei Wang, Tianyu Shi, Yuankai Wu, Luis Miranda-Moreno, and Lijun Sun · 2020
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang · 2021
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Learning to ground multi-agent communication with autoencoders
Toru Lin, Jacob Huh, Christopher Stauffer, Ser Nam Lim, and Phillip Isola · 2021
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Automatic prompt optimization with "gradient descent" and beam search
Reid Pryzant, Dan Iter, Jerry Li, Yin Tat Lee, Chenguang Zhu, and Michael Zeng · 2023
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Visual adversarial examples jailbreak aligned large language models
Xiangyu Qi, Kaixuan Huang, Ashwinee Panda, Mengdi Wang, and Prateek Mittal · 2023
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Hugginggpt: Solving ai tasks with chatgpt and its friends in huggingface
Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, and Yueting Zhuang · 2023
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Large language models can be easily distracted by irrelevant context
Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed H Chi, Nathanael Schärli, and Denny Zhou · 2023
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Reflexion: Language agents with verbal reinforcement learning
Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik R Narasimhan, and Shunyu Yao · 2023
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Xavier Puig, Tianmin Shu, Shuang Li, Zilin Wang, Yuan-Hong Liao, Joshua B. Tenenbaum, Sanja Fidler, and Antonio Torralba · 2021
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Hierarchical multiagent reinforcement learning for allocating guaranteed display ads
Lu Wang, Lei Han, Xinru Chen, Chengchang Li, Junzhou Huang, Weinan Zhang, Wei Zhang, Xiaofeng He, and Dijun Luo · 2021
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Multi-agent reinforcement learning: A selective overview of theories and algorithms
Kaiqing Zhang, Zhuoran Yang, and Tamer Başar · 2021
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Training a helpful and harmless assistant with reinforcement learning from human feedback
Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, et al · 2022
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Multi-agent deep reinforcement learning: a survey
Sven Gronauer and Klaus Diepold · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
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Adaplanner: Adaptive planning from feedback with language models
Haotian Sun, Yuchen Zhuang, Lingkai Kong, Bo Dai, and Chao Zhang · 2023
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Multi-agent collaboration: Harnessing the power of intelligent llm agents
Yashar Talebirad and Amirhossein Nadiri · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
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How do humans overcome individual computational limitations by working together?
Natalia Vélez, Brian Christian, Mathew Hardy, Bill D Thompson, and Thomas L Griffiths · 2023
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Voyager: An open-ended embodied agent with large language models
Guanzhi Wang, Yuqi Xie, Yunfan Jiang, Ajay Mandlekar, Chaowei Xiao, Yuke Zhu, Linxi Fan, and Anima Anandkumar · 2023
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Autogen: Enabling next-gen llm applications via multi-agent conversation framework
Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Shaokun Zhang, Erkang Zhu, Beibin Li, Li Jiang, Xiaoyun Zhang, and Chi Wang · 2023
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Language agents with reinforcement learning for strategic play in the werewolf game
Zelai Xu, Chao Yu, Fei Fang, Yu Wang, and Yi Wu · 2023
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Large language models as optimizers
Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V Le, Denny Zhou, and Xinyun Chen · 2023
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Yi Zheng, Chongyang Ma, Kanle Shi, and Haibin Huang · 2023
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Xizhou Zhu, Yuntao Chen, Hao Tian, Chenxin Tao, Weijie Su, Chenyu Yang, Gao Huang, Bin Li, Lewei Lu, Xiaogang Wang, et al · 2023
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Universal and transferable adversarial attacks on aligned language models
Andy Zou, Zifan Wang, J Zico Kolter, and Matt Fredrikson · 2023
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S-agents: self-organizing agents in open-ended environment
Jiaqi Chen, Yuxian Jiang, Jiachen Lu, and Li Zhang · 2024
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Yoichi Ishibashi and Yoshimasa Nishimura · 2024
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Hierarchical auto-organizing system for open-ended multi-agent navigation
Zhonghan Zhao, Kewei Chen, Dongxu Guo, Wenhao Chai, Tian Ye, Yanting Zhang, and Gaoang Wang · 2024
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