Emergence of Collaborative Hunting via Multi-Agent Deep Reinforcement Learning
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In the multi-agent setting, reinforcement learning has been used to study animal behaviour in areas including predatory-prey dynamics and collective/swarm movement.
“In the multi-agent setting, it has been used to study animal behaviour in areas including predatory-prey dynamics [43,44] and collective/swarm movement [45,46].”
From Maynard Smith revisited: A multi-agent reinforcement learning approach to the coevolution of signalling behaviour (2025) · cites this paper as [43]
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References 45 · Cited by 5
Research works printed but not linked: 6
- S. K. Gazda, R. C. Connor, R. K. Edgar, and F. Cox, "A division of labour with role specialization in group-hunting bottlenose dolphins (tursiops truncatus) off cedar key, florida," Proceedings of the Royal Society B: Biological Sciences, vol. 272, no. 1559, pp. 135-140, 2005.
- R. S. Sutton and A. G. Barto, Reinforcement learning: An introduction. MIT press, 2018.
- H. Hasselt, "Double q-learning," Advances in neural information pro- cessing systems, vol. 23, 2010.
- Z. Wang, T. Schaul, M. Hessel, H. Hasselt, M. Lanctot, and N. Freitas, "Dueling network architectures for deep reinforcement learning," in International conference on machine learning, pp. 1995-2003, PMLR, 2016.
- R. Lowe, Y. I. Wu, A. Tamar, J. Harb, O. Pieter Abbeel, and I. Mordatch, "Multi-agent actor-critic for mixed cooperative-competitive environ- ments," Advances in neural information processing systems, vol. 30, 2017.
- X. Glorot, A. Bordes, and Y. Bengio, "Deep sparse rectifier neural networks," in Proceedings of the fourteenth international conference on artificial intelligence and statistics, pp. 315-323, JMLR Workshop and Conference Proceedings, 2011.
- Authors
- Tsutsui, K., Takeda, K., & Fujii, K.
- Affiliations
- Nagoya University (JP)
- RIKEN (JP)
- Corresponding author
- Tsutsui, Kazushi
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2023
- Volume
- 13643
- Pages
- 210-224
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4385384933
- Cited by
- 5
- References
- 45
- Citations by year
- 2025: 4 · 2024: 1
- FWCI
- 0.79
- Type
- conference paper
- Language
- en
- Open access
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- Institutional Repositories DataBase (IRDB) (submittedVersion, cc-by-nc-nd)
- Nagoya University Repository (NAGOYA Repository) (submittedVersion, cc-by-nc-nd)
- Funders
- Japan Society for the Promotion of Science (20H04075, 22K17673, 21H04892)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Social Sciences › Sociology and Political Science › Evolutionary Game Theory and Cooperation
- Physical Sciences › Computer Science › Artificial Intelligence › Multi-Agent Systems and Negotiation
- Keywords
- cooperative hunting, emergence of cooperation, multi-agent deep reinforcement learning, predator-prey simulation, role division, evolution of sociality, animal behavior
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