no code implementations • 10 Jan 2024 • Jiechuan Jiang, Kefan Su, Zongqing Lu
Cooperative multi-agent reinforcement learning is a powerful tool to solve many real-world cooperative tasks, but restrictions of real-world applications may require training the agents in a fully decentralized manner.
no code implementations • 6 Nov 2022 • Kefan Su, Zongqing Lu
In this paper, we propose \textit{decentralized policy optimization} (DPO), a decentralized actor-critic algorithm with monotonic improvement and convergence guarantee.
no code implementations • CVPR 2023 • Zhaozhi Wang, Kefan Su, Jian Zhang, Huizhu Jia, Qixiang Ye, Xiaodong Xie, Zongqing Lu
In this paper, we propose multi-agent automated machine learning (MA2ML) with the aim to effectively handle joint optimization of modules in automated machine learning (AutoML).
no code implementations • 26 Sep 2022 • Ziluo Ding, Kefan Su, Weixin Hong, Liwen Zhu, Tiejun Huang, Zongqing Lu
Communication helps agents to obtain information about others so that better coordinated behavior can be learned.
no code implementations • 17 Sep 2022 • Kefan Su, Siyuan Zhou, Jiechuan Jiang, Chuang Gan, Xiangjun Wang, Zongqing Lu
Decentralized learning has shown great promise for cooperative multi-agent reinforcement learning (MARL).
no code implementations • 1 Oct 2021 • Kefan Su, Zongqing Lu
Though divergence regularization has been proposed to settle this problem, it cannot be trivially applied to cooperative multi-agent reinforcement learning (MARL).
Multi-agent Reinforcement Learning reinforcement-learning +2