Fetching the paper…
Reading the bibliography…
In recent years, Large Language Models (LLMs) have shown great abilities in various tasks, including question answering, arithmetic problem solving, and poem writing, among others.
D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning internal representations by error propagation, parallel distributed processing, explorations in the microstructure of cognition, ed. de rumelhart and j. mcclelland. vol. 1. 1986,” Biometrika
1986
Earlier work this paper cites.
M. I. Jordan, “Serial order: A parallel distributed processing approach,” in Advances in psychology
1997
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation
1997
Earlier work this paper cites.
2016
Earlier work this paper cites.
Springer, 2016
F. A. Oliehoek, C. Amato, et al · 2016
Earlier work this paper cites.
J. Foerster, I. A. Assael, N. De Freitas, and S. Whiteson, “Learning to communicate with deep multi-agent reinforcement learning,” Advances in neural information processing systems
2016
Earlier work this paper cites.
S. Sukhbaatar, R. Fergus, et al
2016
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems
2017
Earlier work this paper cites.
R. Lowe, Y. I. Wu, A. Tamar, J. Harb, O. Pieter Abbeel, and I. Mordatch, “Multi-agent actor-critic for mixed cooperative-competitive environments,” Advances in neural information processing systems
2017
Earlier work this paper cites.
Y. Hoshen, “Vain: Attentional multi-agent predictive modeling,” Advances in neural information processing systems
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
H. Liang, L. Yang, H. Cheng, W. Tu, and M. Xu, “Human-in-the-loop reinforcement learning,” in 2017 Chinese Automation Congress (CAC)
2017
Earlier work this paper cites.
P. F. Christiano, J. Leike, T. Brown, M. Martic, S. Legg, and D. Amodei, “Deep reinforcement learning from human preferences,” Advances in neural information processing systems
2017
Earlier work this paper cites.
J. A. Calvo and I. Dusparic, “Heterogeneous multi-agent deep reinforcement learning for traffic lights control.,” in AICS
2018
Earlier work this paper cites.
P. Sunehag, G. Lever, A. Gruslys, W. M. Czarnecki, V. Zambaldi, M. Jaderberg, M. Lanctot, N. Sonnerat, J. Z. Leibo, K. Tuyls, et al
2018
Earlier work this paper cites.
J. Jiang and Z. Lu, “Learning attentional communication for multi-agent cooperation,” Advances in neural information processing systems
2018
Earlier work this paper cites.
I. Mordatch and P. Abbeel, “Emergence of grounded compositional language in multi-agent populations,” in Proceedings of the AAAI conference on artificial intelligence
2018
Earlier work this paper cites.
Y. Jiang, S. S. Gu, K. P. Murphy, and C. Finn, “Language as an abstraction for hierarchical deep reinforcement learning,” Advances in Neural Information Processing Systems
2019
Earlier work this paper cites.
P. Hernandez-Leal, B. Kartal, and M. E. Taylor, “A survey and critique of multiagent deep reinforcement learning,” Autonomous Agents and Multi-Agent Systems
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
K. Son, D. Kim, W. J. Kang, D. E. Hostallero, and Y. Yi, “Qtran: Learning to factorize with transformation for cooperative multi-agent reinforcement learning,” in International conference on machine learning
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
A. Das, T. Gervet, J. Romoff, D. Batra, D. Parikh, M. Rabbat, and J. Pineau, “Tarmac: Targeted multi-agent communication,” in International Conference on machine learning
2019
Earlier work this paper cites.
V. Sadhu, C. Sun, A. Karimian, R. Tron, and D. Pompili, “Aerial-deepsearch: Distributed multi-agent deep reinforcement learning for search missions,” in 2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)
2020
Earlier work this paper cites.
T. T. Nguyen, N. D. Nguyen, and S. Nahavandi, “Deep reinforcement learning for multiagent systems: A review of challenges, solutions, and applications,” IEEE transactions on cybernetics
2020
Earlier work this paper cites.
T. Rashid, M. Samvelyan, C. S. De Witt, G. Farquhar, J. Foerster, and S. Whiteson, “Monotonic value function factorisation for deep multi-agent reinforcement learning,” The Journal of Machine Learning Research
2020
Earlier work this paper cites.
T. Rashid, G. Farquhar, B. Peng, and S. Whiteson, “Weighted qmix: Expanding monotonic value function factorisation for deep multi-agent reinforcement learning,” Advances in neural information processing systems
2020
Earlier work this paper cites.
Y. Wang, B. Han, T. Wang, H. Dong, and C. Zhang, “Dop: Off-policy multi-agent decomposed policy gradients,” in International conference on learning representations
2020
Earlier work this paper cites.
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2020
Cited alongside, same era.
L. Zhou and K. Small, “Inverse reinforcement learning with natural language goals,” in Proceedings of the AAAI Conference on Artificial Intelligence
2021
Cited alongside, same era.
A. Brohan, Y. Chebotar, C. Finn, K. Hausman, A. Herzog, D. Ho, J. Ibarz, A. Irpan, E. Jang, R. Julian, et al
2023
Later among the works it cites.
2023
Later among the works it cites.
O. Slumbers, D. H. Mguni, K. Shao, and J. Wang, “Leveraging large language models for optimised coordination in textual multi-agent reinforcement learning,” 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2021
Cited alongside, same era.
T. Zhang, Y. Li, C. Wang, G. Xie, and Z. Lu, “Fop: Factorizing optimal joint policy of maximum-entropy multi-agent reinforcement learning,” in International Conference on Machine Learning
2021
Cited alongside, same era.
S. Shen, Y. Fu, H. Su, H. Pan, P. Qiao, Y. Dou, and C. Wang, “Graphcomm: A graph neural network based method for multi-agent reinforcement learning,” in ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2021
Cited alongside, same era.
D. Hendrycks, C. Burns, S. Kadavath, A. Arora, S. Basart, E. Tang, D. Song, and J. Steinhardt, “Measuring mathematical problem solving with the math dataset,” NeurIPS
2021
Cited alongside, same era.
D. Hendrycks, C. Burns, S. Basart, A. Critch, J. Li, D. Song, and J. Steinhardt, “Aligning ai with shared human values,” Proceedings of the International Conference on Learning Representations (ICLR)
2021
Cited alongside, same era.
M. Chen, J. Tworek, H. Jun, Q. Yuan, H. P. de Oliveira Pinto, J. Kaplan, H. Edwards, Y. Burda, N. Joseph, G. Brockman, A. Ray, R. Puri, G. Krueger, M. Petrov, H. Khlaaf, G. Sastry, P. Mishkin, B. Chan, S. Gray, N. Ryder, M. Pavlov, A. Power, L. Kaiser, M. Bavarian, C. Winter, P. Tillet, F. P. Such, D. Cummings, M. Plappert, F. Chantzis, E. Barnes, A. Herbert-Voss, W. H. Guss, A. Nichol, A. Paino, N. Tezak, J. Tang, I. Babuschkin, S. Balaji, S. Jain, W. Saunders, C. Hesse, A. N. Carr, J. Leike, J. Achiam, V. Misra, E. Morikawa, A. Radford, M. Knight, M. Brundage, M. Murati, K. Mayer, P. Welinder, B. McGrew, D. Amodei, S. McCandlish, I. Sutskever, and W. Zaremba, “Evaluating large language models trained on code,” 2021
2021
Cited alongside, same era.
S. K. Ramakrishnan, A. Gokaslan, E. Wijmans, O. Maksymets, A. Clegg, J. M. Turner, E. Undersander, W. Galuba, A. Westbury, A. X. Chang, M. Savva, Y. Zhao, and D. Batra, “Habitat-matterport 3d dataset (HM3d): 1000 large-scale 3d environments for embodied AI,” in Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
S. Hong, M. Zhuge, J. Chen, X. Zheng, Y. Cheng, J. Wang, C. Zhang, Z. Wang, S. K. S. Yau, Z. Lin, et al
2023
Later among the works it cites.
2023
Later among the works it cites.
J. S. Park, J. O’Brien, C. J. Cai, M. R. Morris, P. Liang, and M. S. Bernstein, “Generative agents: Interactive simulacra of human behavior,” in Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology
2023
Later among the works it cites.
A. Szot, U. Jain, D. Batra, Z. Kira, R. Desai, and A. Rai, “Adaptive coordination in social embodied rearrangement,” in International Conference on Machine Learning
2023
Later among the works it cites.
B. Luo, Z. Wu, F. Zhou, and B.-C. Wang, “Human-in-the-loop reinforcement learning in continuous-action space,” IEEE Transactions on Neural Networks and Learning Systems
2023
Later among the works it cites.
Accessed: 2024-04-22
OpenAI, “ChatGPT: Optimizing Language Models for Dialogue.” https://www.openai.com/chatgpt , 2023 · 2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
N. Shinn, F. Cassano, A. Gopinath, K. Narasimhan, and S. Yao, “Reflexion: Language agents with verbal reinforcement learning,” Advances in Neural Information Processing Systems
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
W. Yao, S. Heinecke, J. C. Niebles, Z. Liu, Y. Feng, L. Xue, R. R. N, Z. Chen, J. Zhang, D. Arpit, R. Xu, P. L. Mui, H. Wang, C. Xiong, and S. Savarese, “Retroformer: Retrospective large language agents with policy gradient optimization,” in The Twelfth International Conference on Learning Representations
2024
Closest in time.
H. Zhang, W. Du, J. Shan, Q. Zhou, Y. Du, J. B. Tenenbaum, T. Shu, and C. Gan, “Building cooperative embodied agents modularly with large language models,” in The Twelfth International Conference on Learning Representations
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
G. Li, H. Hammoud, H. Itani, D. Khizbullin, and B. Ghanem, “Camel: Communicative agents for “mind” exploration of large language model society,” Advances in Neural Information Processing Systems
2024
Closest in time.
Y. Xin, J. Du, Q. Wang, K. Yan, and S. Ding, “Mmap: Multi-modal alignment prompt for cross-domain multi-task learning,” in Proceedings of the AAAI Conference on Artificial Intelligence
2024
Closest in time.
Y. Xin, J. Du, Q. Wang, Z. Lin, and K. Yan, “Vmt-adapter: Parameter-efficient transfer learning for multi-task dense scene understanding,” in Proceedings of the AAAI Conference on Artificial Intelligence
2024
Closest in time.