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Large Language Models (LLMs) are increasingly being used for interactive decision-making tasks requiring planning and adapting to the environment.
Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al. 2019 · 1910
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Htn planning: Complexity and expressivity
Kutluhan Erol, James Hendler, and Dana S Nau. 1994 · 1994
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Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning
Richard S Sutton, Doina Precup, and Satinder Singh. 1999 · 1999
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Recent advances in hierarchical reinforcement learning
Andrew G Barto and Sridhar Mahadevan. 2003 · 2003
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Automated Planning: theory and practice
Malik Ghallab, Dana Nau, and Paolo Traverso. 2004 · 2004
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An overview of hierarchical task network planning
Ilche Georgievski and Marco Aiello. 2014 · 2014
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Back to the blocks world: Learning new actions through situated human-robot dialogue
Lanbo She, Shaohua Yang, Yu Cheng, Yunyi Jia, Joyce Chai, and Ning Xi. 2014 · 2014
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Neural module networks
Jacob Andreas, Marcus Rohrbach, Trevor Darrell, and Dan Klein. 2016 · 2016
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Textworld: A learning environment for text-based games
Marc-Alexandre Côté, Akos Kádár, Xingdi Yuan, Ben Kybartas, Tavian Barnes, Emery Fine, James Moore, Matthew Hausknecht, Layla El Asri, Mahmoud Adada, et al. 2019 · 2018
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Neural sketch learning for conditional program generation
Vijayaraghavan Murali, Letao Qi, Swarat Chaudhuri, and Chris Jermaine. 2018 · 2018
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Data-efficient hierarchical reinforcement learning
Ofir Nachum, Shixiang Shane Gu, Honglak Lee, and Sergey Levine. 2018 · 2018
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The web as a knowledge-base for answering complex questions
Alon Talmor and Jonathan Berant. 2018 · 2018
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Neural module networks for reasoning over text
Nitish Gupta, Kevin Lin, Dan Roth, Sameer Singh, and Matt Gardner. 2019 · 2019
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Self-assembling modular networks for interpretable multi-hop reasoning
Yichen Jiang and Mohit Bansal. 2019 · 2019
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Multi-hop reading comprehension through question decomposition and rescoring
Sewon Min, Victor Zhong, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2019 · 2019
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Learning to infer program sketches
Maxwell Nye, Luke Hewitt, Joshua Tenenbaum, and Armando Solar-Lezama. 2019 · 2019
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Hddl: An extension to pddl for expressing hierarchical planning problems
Daniel Höller, Gregor Behnke, Pascal Bercher, Susanne Biundo, Humbert Fiorino, Damien Pellier, and Ron Alford. 2020 · 2020
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Unsupervised question decomposition for question answering
Ethan Perez, Patrick Lewis, Wen-tau Yih, Kyunghyun Cho, and Douwe Kiela. 2020 · 2020
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Alfred: A benchmark for interpreting grounded instructions for everyday tasks
Mohit Shridhar, Jesse Thomason, Daniel Gordon, Yonatan Bisk, Winson Han, Roozbeh Mottaghi, Luke Zettlemoyer, and Dieter Fox. 2020 · 2020
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Wordcraft: a human-ai collaborative editor for story writing
Andy Coenen, Luke Davis, Daphne Ippolito, Emily Reif, and Ann Yuan. 2021 · 2021
Cited alongside, same era.
Text modular networks: Learning to decompose tasks in the language of existing models
Tushar Khot, Daniel Khashabi, Kyle Richardson, Peter Clark, and Ashish Sabharwal. 2021 · 2021
Cited alongside, same era.
ALFWorld: Aligning Text and Embodied Environments for Interactive Learning
Mohit Shridhar, Xingdi Yuan, Marc-Alexandre Côté, Yonatan Bisk, Adam Trischler, and Matthew Hausknecht. 2021 · 2021
Cited alongside, same era.
Hierarchical reinforcement learning by discovering intrinsic options
Jesse Zhang, Haonan Yu, and Wei Xu. 2021 · 2021
Cited alongside, same era.
Do as i can, not as i say: Grounding language in robotic affordances
Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe. 2023 · 2023
Closest in time.
Swiftsage: A generative agent with fast and slow thinking for complex interactive tasks
Bill Yuchen Lin, Yicheng Fu, Karina Yang, Prithviraj Ammanabrolu, Faeze Brahman, Shiyu Huang, Chandra Bhagavatula, Yejin Choi, and Xiang Ren. 2023 · 2023
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Agentbench: Evaluating llms as agents
Xiao Liu, Hao Yu, Hanchen Zhang, Yifan Xu, Xuanyu Lei, Hanyu Lai, Yu Gu, Hangliang Ding, Kaiwen Men, Kejuan Yang, et al. 2023 · 2023
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Self-refine: Iterative refinement with self-feedback
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al. 2023 · 2023
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Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Chuyuan Fu, Keerthana Gopalakrishnan, Karol Hausman, et al. 2022 · 2022
Cited alongside, same era.
A persistent spatial semantic representation for high-level natural language instruction execution
Valts Blukis, Chris Paxton, Dieter Fox, Animesh Garg, and Yoav Artzi. 2022 · 2022
Cited alongside, same era.
David Dohan, Winnie Xu, Aitor Lewkowycz, Jacob Austin, David Bieber, Raphael Gontijo Lopes, Yuhuai Wu, Henryk Michalewski, Rif A Saurous, Jascha Sohl-Dickstein, et al. 2022 · 2022
Cited alongside, same era.
Minedojo: Building open-ended embodied agents with internet-scale knowledge
Linxi Fan, Guanzhi Wang, Yunfan Jiang, Ajay Mandlekar, Yuncong Yang, Haoyi Zhu, Andrew Tang, De-An Huang, Yuke Zhu, and Anima Anandkumar. 2022 · 2022
Cited alongside, same era.
Language models (mostly) know what they know
Saurav Kadavath, Tom Conerly, Amanda Askell, Tom Henighan, Dawn Drain, Ethan Perez, Nicholas Schiefer, Zac Hatfield-Dodds, Nova DasSarma, Eli Tran-Johnson, et al. 2022 · 2022
Cited alongside, same era.
Film: Following instructions in language with modular methods
So Yeon Min, Devendra Singh Chaplot, Pradeep Kumar Ravikumar, Yonatan Bisk, and Ruslan Salakhutdinov. 2022 · 2022
Cited alongside, same era.
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 · 2022
Cited alongside, same era.
Reinforcement learning with sparse rewards using guidance from offline demonstration
Desik Rengarajan, Gargi Vaidya, Akshay Sarvesh, Dileep Kalathil, and Srinivas Shakkottai. 2022 · 2022
Cited alongside, same era.
OpenAI. 2023 · 2023
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Toolllm: Facilitating large language models to master 16000+ real-world apis
Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, et al. 2023 · 2023
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Imanol Schlag, Sainbayar Sukhbaatar, Asli Celikyilmaz, Wen-tau Yih, Jason Weston, Jürgen Schmidhuber, and Xian Li. 2023 · 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 Huai hsin Chi, Nathanael Scharli, and Denny Zhou. 2023 · 2023
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Reflexion: Language agents with verbal reinforcement learning
Noah Shinn, Federico Cassano, Beck Labash, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. 2023 · 2023
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The art of llm refinement: Ask, refine, and trust
Kumar Shridhar, Koustuv Sinha, Andrew Cohen, Tianlu Wang, Ping Yu, Ram Pasunuru, Mrinmaya Sachan, Jason Weston, and Asli Celikyilmaz. 2023 · 2023
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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. 2023 · 2023
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Llm-planner: Few-shot grounded planning for embodied agents with large language models
Chan Hee Song, Jiaman Wu, Clayton Washington, Brian M Sadler, Wei-Lun Chao, and Yu Su. 2023 · 2023
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Gpt-4 doesn’t know it’s wrong: An analysis of iterative prompting for reasoning problems
Kaya Stechly, Matthew Marquez, and Subbarao Kambhampati. 2023 · 2023
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Pearl: Prompting large language models to plan and execute actions over long documents
Simeng Sun, Y. Liu, Shuo Wang, Chenguang Zhu, and Mohit Iyyer. 2023 · 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 · 2023
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Lemur: Harmonizing natural language and code for language agents
Yiheng Xu, Hongjin Su, Chen Xing, Boyu Mi, Qian Liu, Weijia Shi, Binyuan Hui, Fan Zhou, Yitao Liu, Tianbao Xie, Zhoujun Cheng, Siheng Zhao, Lingpeng Kong, Bailin Wang, Caiming Xiong, and Tao Yu. 2023 · 2023
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Intercode: Standardizing and benchmarking interactive coding with execution feedback
John Yang, Akshara Prabhakar, Karthik Narasimhan, and Shunyu Yao. 2023 · 2023
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Outline, then details: Syntactically guided coarse-to-fine code generation
Wenqing Zheng, SP Sharan, Ajay Kumar Jaiswal, Kevin Wang, Yihan Xi, Dejia Xu, and Zhangyang Wang. 2023 · 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 · 2023
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