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Recent studies have uncovered the potential of Large Language Models (LLMs) in addressing complex sequential decision-making tasks through the provision of high-level instructions.
Learning from demonstration
Stefan Schaal · 1996
Earlier work this paper cites.
Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning
Richard S Sutton, Doina Precup, and Satinder Singh · 1999
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Interactively shaping agents via human reinforcement: The tamer framework
W Bradley Knox and Peter Stone · 2009
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Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Proximal policy optimization algorithms
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Kickstarting deep reinforcement learning
Simon Schmitt, Jonathan J Hudson, Augustin Zidek, Simon Osindero, Carl Doersch, Wojciech M Czarnecki, Joel Z Leibo, Heinrich Kuttler, Andrew Zisserman, Karen Simonyan, et al · 2018
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
Earlier work this paper cites.
Dd-ppo: Learning near-perfect pointgoal navigators from 2.5 billion frames
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Earlier work this paper cites.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Earlier work this paper cites.
Agents teaching agents: a survey on inter-agent transfer learning
Felipe Leno Da Silva, Garrett Warnell, Anna Helena Reali Costa, and Peter Stone · 2020
Earlier work this paper cites.
Habitat 2.0: Training home assistants to rearrange their habitat
Andrew Szot, Alexander Clegg, and al et · 2021
Earlier work this paper cites.
Reincarnating reinforcement learning: Reusing prior computation to accelerate progress
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C Courville, and Marc Bellemare · 2022
Earlier work this paper cites.
Do as I can, not as I say: Grounding language in robotic affordances
Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Chuyuan Fu, Keerthana Gopalakrishnan, Karol Hausman, et al · 2022
Earlier work this paper cites.
Glm: General language model pretraining with autoregressive blank infilling
Zhengxiao Du, Yujie Qian, Xiao Liu, Ming Ding, Jiezhong Qiu, Zhilin Yang, and Jie Tang · 2022
Earlier work this paper cites.
Language models as zero-shot planners: Extracting actionable knowledge for embodied agents
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Hierarchical kickstarting for skill transfer in reinforcement learning
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Model-free reinforcement learning from expert demonstrations: a survey
Jorge Ramírez, Wen Yu, and Adolfo Perrusquía · 2022
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Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, and al et · 2022
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Chain of thought prompting elicits reasoning in large language models
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Reward design with language models
Minae Kwon, Sang Michael Xie, Kalesha Bullard, and Dorsa Sadigh · 2023
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Gpt-driver: Learning to drive with gpt
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Kolby Nottingham, Yasaman Razeghi, Kyungmin Kim, JB Lanier, Pierre Baldi, Roy Fox, and Sameer Singh · 2023
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Languagempc: Large language models as decision makers for autonomous driving
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Reflexion: Language agents with verbal reinforcement learning
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React: Synergizing reasoning and acting in language models
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Tell me where to go: A composable framework for context-aware embodied robot navigation
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Maxime Chevalier-Boisvert, Bolun Dai, and al et · 2023
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Augmenting autotelic agents with large language models
Cédric Colas, Laetitia Teodorescu, Pierre-Yves Oudeyer, Xingdi Yuan, and Marc-Alexandre Côté · 2023
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Palm-e: An embodied multimodal language model
Danny Driess, Fei Xia, Mehdi SM Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Tompson, Quan Vuong, Tianhe Yu, et al · 2023
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Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik R Narasimhan, and Shunyu Yao · 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
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Jump-start reinforcement learning
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Voyager: An open-ended embodied agent with large language models
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A survey on large language model based autonomous agents
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The rise and potential of large language model based agents: A survey
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Language to rewards for robotic skill synthesis
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Enabling intelligent interactions between an agent and an llm: A reinforcement learning approach
Bin Hu, Chenyang Zhao, Pu Zhang, Zihao Zhou, Yuanhang Yang, Zenglin Xu, and Bin Liu · 2024
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