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The captivating realm of Minecraft has attracted substantial research interest in recent years, serving as a rich platform for developing intelligent agents capable of functioning in open-world environments.
A deep hierarchical approach to lifelong learning in minecraft
C. Tessler, S. Givony, T. Zahavy, D. Mankowitz, and S. Mannor · 2017
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Scaling imitation learning in minecraft
A. Amiranashvili, N. Dorka, W. Burgard, V. Koltun, and T. Brox · 2020
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Retrospective analysis of the 2019 minerl competition on sample efficient reinforcement learning
S. Milani, N. Topin, B. Houghton, W. H. Guss, S. P. Mohanty, K. Nakata, O. Vinyals, and N. S. Kuno · 2020
Earlier work this paper cites.
Towards robust and domain agnostic reinforcement learning competitions: Minerl 2020
W. H. Guss, S. Milani, N. Topin, B. Houghton, S. Mohanty, A. Melnik, A. Harter, B. Buschmaas, B. Jaster, C. Berganski, et al · 2021
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Multi-task curriculum learning in a complex, visual, hard-exploration domain: Minecraft
I. Kanitscheider, J. Huizinga, D. Farhi, W. H. Guss, B. Houghton, R. Sampedro, P. Zhokhov, B. Baker, A. Ecoffet, J. Tang, et al · 2021
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Juewu-mc: Playing minecraft with sample-efficient hierarchical reinforcement learning
Z. Lin, J. Li, J. Shi, D. Ye, Q. Fu, and W. Yang · 2021
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Hierarchical deep q-network from imperfect demonstrations in minecraft
A. Skrynnik, A. Staroverov, E. Aitygulov, K. Aksenov, V. Davydov, and A. I. Panov · 2021
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Open-ended learning leads to generally capable agents
O. E. L. Team, A. Stooke, A. Mahajan, C. Barros, C. Deck, J. Bauer, J. Sygnowski, M. Trebacz, M. Jaderberg, M. Mathieu, et al · 2021
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Video pretraining (vpt): Learning to act by watching unlabeled online videos
B. Baker, I. Akkaya, P. Zhokov, J. Huizinga, J. Tang, A. Ecoffet, B. Houghton, R. Sampedro, and J. Clune · 2022
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Minedojo: Building open-ended embodied agents with internet-scale knowledge
L. Fan, G. Wang, Y. Jiang, A. Mandlekar, Y. Yang, H. Zhu, A. Tang, D.-A. Huang, Y. Zhu, and A. Anandkumar · 2022
Cited alongside, same era.
Minerl diamond 2021 competition: Overview, results, and lessons learned
A. Kanervisto, S. Milani, K. Ramanauskas, N. Topin, Z. Lin, J. Li, J. Shi, D. Ye, Q. Fu, W. Yang, et al · 2022
Cited alongside, same era.
Code as policies: Language model programs for embodied control
J. Liang, W. Huang, F. Xia, P. Xu, K. Hausman, B. Ichter, P. Florence, and A. Zeng · 2022
Cited alongside, same era.
Seihai: A sample-efficient hierarchical ai for the minerl competition
H. Mao, C. Wang, X. Hao, Y. Mao, Y. Lu, C. Wu, J. Hao, D. Li, and P. Tang · 2022
Cited alongside, same era.
Deep learning, reinforcement learning, and world models
Y. Matsuo, Y. LeCun, M. Sahani, D. Precup, D. Silver, M. Sugiyama, E. Uchibe, and J. Morimoto · 2022
Mastering diverse domains through world models
D. Hafner, J. Pasukonis, J. Ba, and T. Lillicrap · 2023
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Api-bank: A benchmark for tool-augmented llms
M. Li, F. Song, B. Yu, H. Yu, Z. Li, F. Huang, and Y. Li · 2023
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S. Milani, A. Kanervisto, K. Ramanauskas, S. Schulhoff, B. Houghton, S. Mohanty, B. Galbraith, K. Chen, Y. Song, T. Zhou, et al · 2023
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Toolformer: Language models can teach themselves to use tools
T. Schick, J. Dwivedi-Yu, R. Dessì, R. Raileanu, M. Lomeli, L. Zettlemoyer, N. Cancedda, and T. Scialom · 2023
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Hugginggpt: Solving ai tasks with chatgpt and its friends in huggingface
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Cited alongside, same era.
Progprompt: Generating situated robot task plans using large language models
I. Singh, V. Blukis, A. Mousavian, A. Goyal, D. Xu, J. Tremblay, D. Fox, J. Thomason, and A. Garg · 2022
Cited alongside, same era.
Chain of thought prompting elicits reasoning in large language models
J. Wei, X. Wang, D. Schuurmans, M. Bosma, E. Chi, Q. Le, and D. Zhou · 2022
Cited alongside, same era.
S. Cai, Z. Wang, X. Ma, A. Liu, and Y. Liang · 2023
Cited alongside, same era.
Palm-e: An embodied multimodal language model
D. Driess, F. Xia, M. S. Sajjadi, C. Lynch, A. Chowdhery, B. Ichter, A. Wahid, J. Tompson, Q. Vuong, T. Yu, et al · 2023
Cited alongside, same era.
Language models as zero-shot planners: Extracting actionable knowledge for embodied agents
W. Huang, P. Abbeel, D. Pathak, and I. Mordatch
Cited in the paper.
Inner monologue: Embodied reasoning through planning with language models
W. Huang, F. Xia, T. Xiao, H. Chan, J. Liang, P. Florence, A. Zeng, J. Tompson, I. Mordatch, Y. Chebotar, et al
Cited in the paper.
Y. Shen, K. Song, X. Tan, D. Li, W. Lu, and Y. Zhuang · 2023
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Z. Wang, S. Cai, A. Liu, X. Ma, and Y. Liang · 2023
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Minecraft — Wikipedia, the free encyclopedia
Wikipedia contributors · 2023
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Plan4mc: Skill reinforcement learning and planning for open-world minecraft tasks
H. Yuan, C. Zhang, H. Wang, F. Xie, P. Cai, H. Dong, and Z. Lu · 2023
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