all AI news
Reaching Consensus in Cooperative Multi-Agent Reinforcement Learning with Goal Imagination
March 6, 2024, 5:42 a.m. | Liangzhou Wang, Kaiwen Zhu, Fengming Zhu, Xinghu Yao, Shujie Zhang, Deheng Ye, Haobo Fu, Qiang Fu, Wei Yang
cs.LG updates on arXiv.org arxiv.org
Abstract: Reaching consensus is key to multi-agent coordination. To accomplish a cooperative task, agents need to coherently select optimal joint actions to maximize the team reward. However, current cooperative multi-agent reinforcement learning (MARL) methods usually do not explicitly take consensus into consideration, which may cause miscoordination problem. In this paper, we propose a model-based consensus mechanism to explicitly coordinate multiple agents. The proposed Multi-agent Goal Imagination (MAGI) framework guides agents to reach consensus with an Imagined …
abstract agent agents arxiv consensus cs.ai cs.lg current imagination key multi-agent reinforcement reinforcement learning team type
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Founding AI Engineer, Agents
@ Occam AI | New York
AI Engineer Intern, Agents
@ Occam AI | US
AI Research Scientist
@ Vara | Berlin, Germany and Remote
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Data Engineer
@ Kaseya | Bengaluru, Karnataka, India