no code implementations • 1 Feb 2024 • Guangzheng Hu, Yuanheng Zhu, Haoran Li, Dongbin Zhao
Based on it, we present a novel multi-agent reinforcement learning framework, Factorized Multi-Agent MiniMax Q-Learning (FM3Q), which can factorize the joint minimax Q function into individual ones and iteratively solve for the IGMM-satisfied minimax Q functions for 2t0sMGs.
no code implementations • 18 Apr 2022 • Menghan Wang, Yuchen Guo, Zhenqi Zhao, Guangzheng Hu, Yuming Shen, Mingming Gong, Philip Torr
To alleviate the influence of the annotation bias, we perform a momentum update to ensure a consistent item representation.
no code implementations • 10 Oct 2020 • Guangzheng Hu, Yuanheng Zhu, Dongbin Zhao, Mengchen Zhao, Jianye Hao
Then the design of the event-triggered strategy is formulated as a constrained Markov decision problem, and reinforcement learning finds the best communication protocol that satisfies the limited bandwidth constraint.
Multiagent Systems