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Recent research has demonstrated the potential of reinforcement learning (RL) in enabling effective multi-robot collaboration, particularly in social dilemmas where robots face a trade-off between self-interests and collective benefits.
R. S. Sutton, D. McAllester, S. Singh, and Y. Mansour, “Policy gradient methods for reinforcement learning with function approximation,” in Advances in Neural Information Processing Systems , S. Solla, T. Leen, and K. Müller, Eds., vol. 12. MIT Press, 1999. [Online]. Available: https://proceedings.neurips.cc/paper/1999/file/464d828b85b0bed98e80ade0a5c43b0f-Paper.pdf
1999
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J. L. Stimpson and M. A. Goodrich, “Learning to cooperate in a social dilemma: A satisficing approach to bargaining,” in ICML . Citeseer, 2003, pp. 728–735
2003
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J. Foerster, R. Y. Chen, M. Al-Shedivat, S. Whiteson, P. Abbeel, and I. Mordatch, “Learning with opponent-learning awareness,” in Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems , ser. AAMAS ’18. Richland, SC: International Foundation for Autonomous Agents and Multiagent Systems, 2018, p. 122–130
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2018
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S. Ahangar, M. V. Mehrabani, A. P. Shorijeh, and M. T. Masouleh, “Design a 3-dof delta parallel robot by one degree redundancy along the conveyor axis, a novel automation approach,” in 2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI) . IEEE, 2019, pp. 413–418
2019
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V. Tereshchuk, J. Stewart, N. Bykov, S. Pedigo, S. Devasia, and A. G. Banerjee, “An efficient scheduling algorithm for multi-robot task allocation in assembling aircraft structures,” IEEE Robotics Autom. Lett. , vol. 4, no. 4, pp. 3844–3851, 2019. [Online]. Available: https://doi.org/10.1109/LRA.2019.2929983
2019
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L. Zhang, Y. Sun, A. Barth, and O. Ma, “Decentralized control of multi-robot system in cooperative object transportation using deep reinforcement learning,” IEEE Access , vol. 8, pp. 184 109–184 119, 2020
2020
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C. Yu, Y. Dong, Y. Li, and Y. Chen, “Distributed multi-agent deep reinforcement learning for cooperative multi-robot pursuit,” The Journal of Engineering , vol. 2020, no. 13, pp. 499–504, 2020
2020
Cited alongside, same era.
J. Yang, A. Li, M. Farajtabar, P. Sunehag, E. Hughes, and H. Zha, “Learning to incentivize other learning agents,” in Proceedings of the 34th International Conference on Neural Information Processing Systems , ser. NIPS’20. Red Hook, NY, USA: Curran Associates Inc., 2020
2020
Cited alongside, same era.
R. Bai, R. Zheng, M. Liu, and S. Zhang, “Multi-robot task planning under individual and collaborative temporal logic specifications,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021, Prague, Czech Republic, September 27 - Oct. 1, 2021 . IEEE, 2021, pp. 6382–6389. [Online]. Available: https://doi.org/10.1109/IROS51168.2021.9636287
2021
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2022
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2022
Later among the works it cites.
Y. Yuan, T. Guo, P. Zhao, and H. Jiang, “Adherence improves cooperation in sequential social dilemmas,” Applied Sciences , vol. 12, no. 16, p. 8004, 2022
2022
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J. Guo, A. Li, and C. Liu, “Backdoor detection and mitigation in competitive reinforcement learning,” 2023
2023
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A. Agrawal, S. H. Arul, A. S. Bedi, and D. Manocha, “DC-MRTA: decentralized multi-robot task allocation and navigation in complex environments,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022, Kyoto, Japan, October 23-27, 2022 . IEEE, 2022, pp. 11 711–11 718. [Online]. Available: https://doi.org/10.1109/IROS47612.2022.9981353
2022
Cited alongside, same era.
Y. Gao, Y. Wang, X. Zhong, T. Yang, M. Wang, Z. Xu, Y. Wang, Y. Lin, C. Xu, and F. Gao, “Meeting-merging-mission: A multi-robot coordinate framework for large-scale communication-limited exploration,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022, Kyoto, Japan, October 23-27, 2022 . IEEE, 2022, pp. 13 700–13 707. [Online]. Available: https://doi.org/10.1109/IROS47612.2022.9981544
2022
Cited alongside, same era.
Q. Zhang, R. Quan, S. Qimuge, P. Xia, J. Wang, X. Zan, F. Wang, C. Chen, Q. Wei, H. Zhao, X. Liu, and F. Qiao, “OCTOANTS: A heterogeneous lightweight intelligent multi-robot collaboration system with resource-constrained iot devices,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022, Kyoto, Japan, October 23-27, 2022 . IEEE, 2022, pp. 2556–2563. [Online]. Available: https://doi.org/10.1109/IROS47612.2022.9982135
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2071
Closest in time.
T. Guo, Y. Yuan, and P. Zhao, “Admission-based reinforcement-learning algorithm in sequential social dilemmas,” Applied Sciences , vol. 13, no. 3, 2023. [Online]. Available: https://www.mdpi.com/2076-3417/13/3/1807
2076
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