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Large Language Models (LLMs) have shown great success as high-level planners for zero-shot game-playing agents.
2006
Earlier work this paper cites.
2021
Earlier work this paper cites.
W. Huang, F. Xia, T. Xiao, H. Chan, J. Liang, P. Florence, A. Zeng, J. Tompson, I. Mordatch, Y. Chebotar, P. Sermanet, N. Brown, T. Jackson, L. Luu, S. Levine, K. Hausman, and B. Ichter, “Inner monologue: Embodied reasoning through planning with language models,” 2022
2022
Earlier work this paper cites.
E. Hambro, S. Mohanty, D. Babaev, M. Byeon, D. Chakraborty, E. Grefenstette, M. Jiang, D. Jo, A. Kanervisto, J. Kim, S. Kim, R. Kirk, V. Kurin, H. Küttler, T. Kwon, D. Lee, V. Mella, N. Nardelli, I. Nazarov, N. Ovsov, J. Parker-Holder, R. Raileanu, K. Ramanauskas, T. Rocktäschel, D. Rothermel, M. Samvelyan, D. Sorokin, M. Sypetkowski, and M. Sypetkowski, “Insights from the neurips 2021 nethack challenge,” 2022
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
H. Naveed, A. U. Khan, S. Qiu, M. Saqib, S. Anwar, M. Usman, N. Akhtar, N. Barnes, and A. Mian, “A comprehensive overview of large language models,” 2023
2023
Earlier work this paper cites.
C. H. Song, J. Wu, C. Washington, B. M. Sadler, W.-L. Chao, and Y. Su, “Llm-planner: Few-shot grounded planning for embodied agents with large language models,” 2023
2023
Earlier work this paper cites.
Z. Wang, S. Cai, A. Liu, Y. Jin, J. Hou, B. Zhang, H. Lin, Z. He, Z. Zheng, Y. Yang, X. Ma, and Y. Liang, “Jarvis-1: Open-world multi-task agents with memory-augmented multimodal language models,” 2023
2023
Cited alongside, same era.
X. Zhu, Y. Chen, H. Tian, C. Tao, W. Su, C. Yang, G. Huang, B. Li, L. Lu, X. Wang, Y. Qiao, Z. Zhang, and J. Dai, “Ghost in the minecraft: Generally capable agents for open-world environments via large language models with text-based knowledge and memory,” 2023
2023
Cited alongside, same era.
G. Wang, Y. Xie, Y. Jiang, A. Mandlekar, C. Xiao, Y. Zhu, L. Fan, and A. Anandkumar, “Voyager: An open-ended embodied agent with large language models,” 2023
2023
Cited alongside, same era.
“autoascend,” GitHub, 10 2023. [Online]. Available: https://github.com/maciej-sypetkowski/autoascend
2023
Cited alongside, same era.
J. Liang, W. Huang, F. Xia, P. Xu, K. Hausman, B. Ichter, P. Florence, and A. Zeng, “Code as policies: Language model programs for embodied control,” 2023
2023
Later among the works it cites.
Y. J. Ma, W. Liang, G. Wang, D.-A. Huang, O. Bastani, D. Jayaraman, Y. Zhu, L. Fan, and A. Anandkumar, “Eureka: Human-level reward design via coding large language models,” 2023
2023
Later among the works it cites.
M. Klissarov, P. D’Oro, S. Sodhani, R. Raileanu, P.-L. Bacon, P. Vincent, A. Zhang, and M. Henaff, “Motif: Intrinsic motivation from artificial intelligence feedback,” 2023
2023
Later among the works it cites.
M. Kwon, S. M. Xie, K. Bullard, and D. Sadigh, “Reward design with language models,” 2023
2023
Later among the works it cites.
H. Touvron and et al., “Llama 2: Open foundation and fine-tuned chat models,” 2023
2023
Later among the works it cites.
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2023
Cited alongside, same era.
Z. Wang, S. Cai, A. Liu, X. Ma, and Y. Liang, “Describe, explain, plan and select: Interactive planning with large language models enables open-world multi-task agents,” 2023
2023
Cited alongside, same era.
K. Lorber, “Nethack home page.” [Online]. Available: https://nethack.org/
Cited in the paper.
maciej sypetkowski, “Autoascend – 1st place nethack agent for the nethack challenge at neurips 2021,” GitHub, 01 2024. [Online]. Available: https://github.com/maciej-sypetkowski/autoascend
2024
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