A journal of IEEE and CAA , publishes
high-quality papers in English on original
theoretical/experimental research
and development in all areas of automation
Volume 11
Issue 1
IEEE/CAA Journal of Automatica Sinica
| Citation: | S. Sun, D. Cai, H.-T. Zhang, and N. Xing, “Reinforcement learning-based MAS interception in antagonistic environments,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 1, pp. 270–272, Jan. 2024. doi: 10.1109/JAS.2023.123798
|
| [1] |
Y. Zheng, A. Lai, X. Yu, and W. Lan, “Early-awareness collision avoidance in optimal multi-agent path planning with temporal logic specifications,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 5, pp. 1346–1348, 2023. doi: 10.1109/JAS.2022.106043
|
| [2] |
B. Ning, Q.-L. Han, Z. Zuo, L. Ding, Q. Lu, and X. Ge, “Fixed-time and prescribed-time consensus control of multiagent systems and its applications: A survey of recent trends and methodologies,” IEEE Trans. Industr. Inform., vol. 19, no. 2, pp. 1121–1135, 2023. doi: 10.1109/TII.2022.3201589
|
| [3] |
R. Lowe, Y. Wu, A. Tamar, J. Harb, I. Pieter, and A. Mordatch, “Multi-agent actor-critic for mixed cooperative-competitive environments,” in Proc. 31st Int. Conf. Neural Inf. Process. Syst., 2017, pp. 6382–6393.
|
| [4] |
R. Zhang, Q. Zong, X. Zhang, L. Dou, and B. Tian, “Game of drones: Multi-UAV pursuit-evasion game with online motion planning by deep reinforcement learning,” IEEE Trans. Neural. Netw. Learn. Syst., vol. 34, no. 10, pp. 7900–7909, Oct. 2023. doi: 10.1109/TNNLS.2022.3146976
|
| [5] |
Y. Yu, J. Liu, and C. Wei, “Hawk and pigeon’s intelligence for UAV swarm dynamic combat game via competitive learning pigeon-inspired optimization,” Sci. China Technol. Sci., vol. 65, no. 5, pp. 1072–1086, 2022. doi: 10.1007/s11431-021-1951-9
|
| [6] |
Z. Zhang and D. Zhao, “Clique-based cooperative multiagent reinforcement learning using factor graphs,” IEEE/CAA J. Autom. Sinica, vol. 1, no. 3, pp. 248–256, 2014. doi: 10.1109/JAS.2014.7004682
|
| [7] |
L. Xue, C. Sun, D. Wunsch, Y. Zhou, and F. Yu, “An adaptive strategy via reinforcement learning for the prisoner’s dilemma game,” IEEE/CAA J. Autom. Sinica, vol. 5, no. 1, pp. 301–310, 2017.
|
| [8] |
T. Rupprecht and Y. Wang, “A survey for deep reinforcement learning in markovian cyber-physical systems: Common problems and solutions,” Neural Netw., vol. 153, pp. 12–36, 2022.
|
| [9] |
L. Huang, M. Fu, H. Qu, S. Wang, and S. Hu, “A deep reinforcement learning-based method applied for solving multi-agent defense and attack problems,” Expert Syst. Appl., vol. 176, p. 114896, 2021. doi: 10.1016/j.eswa.2021.114896
|
| [10] |
B. Wang, S. Li, X. Gao, and T. Xie, “UAV swarm confrontation using hierarchical multiagent reinforcement learning,” Int. J. Aerosp. Eng., vol. 2021, p. 3360116, 2021.
|
| [11] |
T. Zhang, L. Chai, S. Wang, J. Jin, X. Liu, A. Song, and Y. Lan, “Improving autonomous behavior strategy learning in an unmanned swarm system through knowledge enhancement,” IEEE Trans. Reliab., vol. 71, no. 2, pp. 763–774, 2022. doi: 10.1109/TR.2022.3158279
|
| [12] |
Z. Chen and N. Li, “An optimal control-based distributed reinforcement learning framework for a class of non-convex objective functionals of the multi-agent network,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 11, pp. 2081–2093, 2023. doi: 10.1109/JAS.2022.105992
|
Figures(4) / Tables(1)
DownLoad:
