@inproceedings{d35d8a64187d452d91be645df7ab77a8,
title = "Multi-Agent Confrontation Game Based on Multi-Agent Reinforcement Learning",
abstract = "This paper studies the multi-agent confrontation game problem, and takes unmanned aerial vehicle (UAV) offense-defense confrontation as the research object. Deep Deterministic Policy Gradient (DDPG) and Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm are used for policy optimization. The experimental results show that MADDPG can acquire good policy in multi-agent confrontation game environment. In order to make MADDPG suitable for large-scale multi-agent game problems and obtain robust policy, this paper improves MADDPG by introducing Mean Field Theory (MFT) and 'Minimax' idea. The experimental results show that the improved algorithms can deal with large-scale multi-agent game problem and obtain robust policy.",
keywords = "mean field theory, minimax, multi-agent confrontation game, policy gradient",
author = "Shuo Han and Liangjun Ke and Zhigang Wang",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Unmanned Systems, ICUS 2021 ; Conference date: 15-10-2021 Through 17-10-2021",
year = "2021",
doi = "10.1109/ICUS52573.2021.9641171",
language = "英语",
series = "Proceedings of 2021 IEEE International Conference on Unmanned Systems, ICUS 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "157--162",
booktitle = "Proceedings of 2021 IEEE International Conference on Unmanned Systems, ICUS 2021",
}