1 code implementation • 22 Sep 2022 • Chenxu Wang, Fuli Feng, Yang Zhang, Qifan Wang, Xunhan Hu, Xiangnan He
A standard choice is treating the missing data as negative training samples and estimating interaction likelihood between user-item pairs along with the observed interactions.
no code implementations • 5 May 2022 • Mingyu Yang, Jian Zhao, Xunhan Hu, Wengang Zhou, Jiangcheng Zhu, Houqiang Li
In this way, agents dealing with the same subtask share their learning of specific abilities and different subtasks correspond to different specific abilities.
Multi-agent Reinforcement Learning reinforcement-learning +3
1 code implementation • 6 Apr 2022 • Youpeng Zhao, Jian Zhao, Xunhan Hu, Wengang Zhou, Houqiang Li
Recent years have witnessed the great breakthrough of deep reinforcement learning (DRL) in various perfect and imperfect information games.
1 code implementation • 16 Mar 2022 • Jian Zhao, Youpeng Zhao, Weixun Wang, Mingyu Yang, Xunhan Hu, Wengang Zhou, Jianye Hao, Houqiang Li
To the best of our knowledge, this work is the first to study the unexpected crashes in the multi-agent system.
Multi-agent Reinforcement Learning reinforcement-learning +3
1 code implementation • 16 Mar 2022 • Jian Zhao, Xunhan Hu, Mingyu Yang, Wengang Zhou, Jiangcheng Zhu, Houqiang Li
In this way, CTDS balances the full utilization of global observation during training and the feasibility of decentralized execution for online inference.
Multi-agent Reinforcement Learning reinforcement-learning +3
1 code implementation • 21 Feb 2022 • Jian Zhao, Mingyu Yang, Youpeng Zhao, Xunhan Hu, Wengang Zhou, Jiangcheng Zhu, Houqiang Li
Specifically, we model both individual Q-values and global Q-value with categorical distribution.
no code implementations • 9 Feb 2022 • Jian Zhao, Yue Zhang, Xunhan Hu, Weixun Wang, Wengang Zhou, Jianye Hao, Jiangcheng Zhu, Houqiang Li
In cooperative multi-agent systems, agents jointly take actions and receive a team reward instead of individual rewards.