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About this paper
Jiechuan Jiang, Zongqing Lu
ATOC introduces an attentional communication model for Multi-Agent Reinforcement Learning that enables agents to learn effective and efficient communication strategies in large-scale, partially observable environments. The model helps agents develop significantly more coordinated and sophisticated strategies, consistently outperforming baselines across diverse cooperative tasks.
Key insights
- Learning dynamic, need-based communication, where agents decide when to communicate, significantly reduces information overload and enhances efficiency in large-scale MARL.
- Using an attention mechanism to selectively choose collaborators and an LSTM to integrate only important information from communications improves cooperative decision-making.
- The learned attentional communication enables agents to develop complex and diverse cooperative strategies, such as division of labor, even with shared policy network parameters.
Summary by alphaXiv.
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