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Large Language Models (LLM) based agents have shown promise in autonomously completing tasks across various domains, e.g., robotics, games, and web navigation.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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Reinforcement learning on web interfaces using workflow-guided exploration
Evan Zheran Liu, Kelvin Guu, Panupong Pasupat, Tianlin Shi, and Percy Liang · 2018
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Offline reinforcement learning: Tutorial, review, and perspectives on open problems
Sergey Levine, Aviral Kumar, G. Tucker, and Justin Fu · 2020
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Webgpt: Browser-assisted question-answering with human feedback
Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Ouyang Long, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, Xu Jiang, Karl Cobbe, Tyna Eloundou, Gretchen Krueger, Kevin Button, Matthew Knight, Benjamin Chess, and John Schulman · 2021
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Alfworld: Aligning text and embodied environments for interactive learning
Mohit Shridhar, Xingdi Yuan, Marc-Alexandre Côté, Yonatan Bisk, Adam Trischler, and Matthew Hausknecht · 2021
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Code as policies: Language model programs for embodied control
Jacky Liang, Wenlong Huang, F. Xia, Peng Xu, Karol Hausman, Brian Ichter, Peter R. Florence, and Andy Zeng · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke E. Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Francis Christiano, Jan Leike, and Ryan J. Lowe · 2022
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Progprompt: Generating situated robot task plans using large language models
Ishika Singh, Valts Blukis, Arsalan Mousavian, Ankit Goyal, Danfei Xu, Jonathan Tremblay, Dieter Fox, Jesse Thomason, and Animesh Garg · 2022
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Llm-planner: Few-shot grounded planning for embodied agents with large language models
Chan Hee Song, Jiaman Wu, Clay Washington, Brian M. Sadler, Wei-Lun Chao, and Yu Su · 2022
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Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Huai hsin Chi, F. Xia, Quoc Le, and Denny Zhou · 2022
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Large language models are human-level prompt engineers
Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba · 2022
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Metagpt: Meta programming for multi-agent collaborative framework
Sirui Hong, Xiawu Zheng, Jonathan P. Chen, Yuheng Cheng, Ceyao Zhang, Zili Wang, Steven Ka Shing Yau, Zi Hen Lin, Liyang Zhou, Chenyu Ran, Lingfeng Xiao, and Chenglin Wu · 2023
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Language models can solve computer tasks
Geunwoo Kim, Pierre Baldi, and Stephen Marcus McAleer · 2023
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Clin: A continually learning language agent for rapid task adaptation and generalization
Bodhisattwa Prasad Majumder, Bhavana Dalvi, Peter Alexander Jansen, Oyvind Tafjord, Niket Tandon, Li Zhang, Chris Callison-Burch, and Peter Clark · 2023
Cited alongside, same era.
OpenAI · 2023
Cited alongside, same era.
Memgpt: Towards llms as operating systems
Charles Packer, Vivian Fang, Shishir G. Patil, Kevin Lin, Sarah Wooders, and Joseph E. Gonzalez · 2023
Cited alongside, same era.
Generative agents: Interactive simulacra of human behavior
Joon Sung Park, Joseph C. O’Brien, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, and Michael S. Bernstein · 2023
Cited alongside, same era.
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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Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, and Karthik Narasimhan · 2023
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ReAct: Synergizing reasoning and acting in language models
Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao · 2023
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Language agent tree search unifies reasoning acting and planning in language models
Andy Zhou, Kai Yan, Michal Shlapentokh-Rothman, Haohan Wang, and Yu-Xiong Wang · 2023
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Webarena: A realistic web environment for building autonomous agents
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Chen Qian, Xin Cong, Cheng Yang, Weize Chen, Yusheng Su, Juyuan Xu, Zhiyuan Liu, Maosong Sun, and Wei Liu · 2023
Cited alongside, same era.
Reflexion: language agents with verbal reinforcement learning
Noah Shinn, Federico Cassano, Beck Labash, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao · 2023
Cited alongside, same era.
Adaplanner: Adaptive planning from feedback with language models
Haotian Sun, Yuchen Zhuang, Lingkai Kong, Bo Dai, and Chao Zhang · 2023
Cited alongside, same era.
Chatgpt for robotics: Design principles and model abilities
Sai Vemprala, Rogerio Bonatti, Arthur Fender C. Bucker, and Ashish Kapoor · 2023
Cited alongside, same era.
Chatgpt empowered long-step robot control in various environments: A case application
Naoki Wake, Atsushi Kanehira, Kazuhiro Sasabuchi, Jun Takamatsu, and Katsushi Ikeuchi · 2023
Cited alongside, same era.
Voyager: An open-ended embodied agent with large language models
Guanzhi Wang, Yuqi Xie, Yunfan Jiang, Ajay Mandlekar, Chaowei Xiao, Yuke Zhu, Linxi (Jim) Fan, and Anima Anandkumar · 2023
Cited alongside, same era.
Promptagent: Strategic planning with language models enables expert-level prompt optimization
Xinyuan Wang, Chenxi Li, Zhen Wang, Fan Bai, Haotian Luo, Jiayou Zhang, Nebojsa Jojic, Eric P. Xing, and Zhiting Hu · 2023
Cited alongside, same era.
Describe, explain, plan and select: Interactive planning with large language models enables open-world multi-task agents
Zihao Wang, Shaofei Cai, Anji Liu, Xiaojian Ma, and Yitao Liang · 2023
Cited alongside, same era.
Shuyan Zhou, Frank F. Xu, Hao Zhu, Xuhui Zhou, Robert Lo, Abishek Sridhar, Xianyi Cheng, Yonatan Bisk, Daniel Fried, Uri Alon, and Graham Neubig · 2023
Later among the works it cites.
Xizhou Zhu, Yuntao Chen, Hao Tian, Chenxin Tao, Weijie Su, Chenyu Yang, Gao Huang, Bin Li, Lewei Lu, Xiaogang Wang, Y. Qiao, Zhaoxiang Zhang, and Jifeng Dai · 2023
Later among the works it cites.
Large language models can learn rules
Zhaocheng Zhu, Yuan Xue, Xinyun Chen, Denny Zhou, Jian Tang, Dale Schuurmans, and Hanjun Dai · 2023
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Yao Fu, Dong-Ki Kim, Jaekyeom Kim, Sungryull Sohn, Lajanugen Logeswaran, Kyunghoon Bae, and Honglak Lee · 2024
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Rap: Retrieval-augmented planning with contextual memory for multimodal llm agents
Tomoyuki Kagaya, Thong Jing Yuan, Yuxuan Lou, Jayashree Karlekar, Sugiri Pranata, Akira Kinose, Koki Oguri, Felix Wick, and Yang You · 2024
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Step: Stacked llm policies for web actions
Paloma Sodhi, S. R. K. Branavan, Yoav Artzi, and Ryan McDonald · 2024
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Executable code actions elicit better llm agents
Xingyao Wang, Yangyi Chen, Lifan Yuan, Yizhe Zhang, Yunzhu Li, Hao Peng, and Heng Ji · 2024
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Agent-pro: Learning to evolve via policy-level reflection and optimization
Wenqi Zhang, Ke Tang, Hai Wu, Mengna Wang, Yongliang Shen, Guiyang Hou, Zeqi Tan, Peng Li, Yue Ting Zhuang, and Weiming Lu · 2024
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Expel: Llm agents are experiential learners
Andrew Zhao, Daniel Huang, Quentin Xu, Matthieu Lin, Y. Liu, and Gao Huang · 2024
Closest in time.