Page 75 - Read Online
P. 75

Ma et al. Intell Robot 2023;3(4):581-95  I http://dx.doi.org/10.20517/ir.2023.33  Page 15 of 15



                  traffic signal control. Neural Netw 2021;139:265–77. DOI
               18. Zheng P, Xia L, Li C, Li X, Liu B. Towards Self-X cognitive manufacturing network: An industrial knowledge graph-based multi-agent
                  reinforcement learning approach. J Manuf Syst 2021;61:16–26. DOI
               19. Wang J, Yuan M, Li Y, Zhao Z. Hierarchical attention master–slave for heterogeneous multi-agent reinforcement learning. Neural Netw
                  2023;162:359–68. DOI
               20. Wang X, Zhang L, Lin T, et al. Solving job scheduling problems in a resource preemption environment with multi-agent reinforcement
                  learning. Robot Comput-Int Manuf 2022;77:102324. DOI
               21. Bettini M, Shankar A, Prorok A. Heterogeneous multi-robot reinforcement learning. arXiv preprint arXiv:230107137 2023. DOI
               22. Lee ES, Zhou L, Ribeiro A, Kumar V. Graph neural networks for decentralized multi-agent perimeter defense. Front Control Eng
                  2023;4:1104745. DOI
               23. Fernando M, Senanayake R, Choi H, Swany M. Graph Attention Multi-Agent Fleet Autonomy for Advanced Air Mobility. arXiv preprint
                  arXiv:230207337 2023. DOI
               24. Huang J, Su J, Chang Q. Graph neural network and multi-agent reinforcement learning for machine-process-system integrated control to
                  optimize production yield. J Manuf Syst 2022;64:81–93. DOI
               25. Mo X, Huang Z, Xing Y, Lv C. Multi-agent trajectory prediction with heterogeneous edge-enhanced graph attention network. IEEE Trans
                  Intell Transport Syst 2022;23:9554–67. DOI
               26. Jia X, Wu P, Chen L, et al. Hdgt: Heterogeneous driving graph transformer for multi-agent trajectory prediction via scene encoding. IEEE
                  Trans Pattern Anal Mach Intell 2023;45:13860-75. DOI
               27. Deka A, Sycara KP. Natural emergence of heterogeneous strategies in artificially intelligent competitive teams. In: Proceedings of the
                  12th International Conference on Swarm Intelligence. vol. 12689. Qingdao, China: Springer; 2021. pp. 13–25. DOI
               28. Ji X, Li H, Pan Z, Gao X, Tu C. Decentralized, unlabeled multi-agent navigation in obstacle-rich environments using graph neural networks.
                  In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. Prague, Czech Republic: IEEE; 2021. pp.
                  8936–43. DOI
               29. Li M, Chen S, Shen Y, et al. Online multi-agent forecasting with interpretable collaborative graph neural networks. IEEE Trans Neural
                  Netw Learn Syst 2022:1–15. DOI
               30. Ezugwu AE, Frincu ME, Adewumi AO, Buhari SM, Junaidu SB. Neural network-based multi-agent approach for scheduling in distributed
                  systems. Concurrency and Computation: Practice and Experience 2017;29:e3887. DOI
               31. Li J, Ma H, Zhang Z, Li J, Tomizuka M. Spatio-temporal graph dual-attention network for multi-agent prediction and tracking. IEEE
                  Trans Intell Transport Syst 2021;23:10556–69. DOI
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