In numerous artificial intelligence applications, the collaborative efforts of multiple intelligent agents are imperative for the successful attainment of target objectives. To enhance coordination among these agents, a distributed communication framework is often employed. However, information sharing among all agents proves to be resource-intensive, while the adoption of a manually pre-defined communication architecture imposes limitations on inter-agent communication, thereby constraining the potential for collaborative efforts. In this study, we introduce a novel approach wherein we conceptualize the communication architecture among agents as a learnable graph. We formulate this problem as the task of determining the communication graph while enabling the architecture parameters to update normally, thus necessitating a bi-level optimization process. Utilizing continuous relaxation of the graph representation and incorporating attention units, our proposed approach, CommFormer, efficiently optimizes the communication graph and concurrently refines architectural parameters through gradient descent in an end-to-end manner. Extensive experiments on a variety of cooperative tasks substantiate the robustness of our model across diverse cooperative scenarios, where agents are able to develop more coordinated and sophisticated strategies regardless of changes in the number of agents.

翻译：在众多人工智能应用中，多智能体的协同合作对于成功达成目标任务至关重要。为增强智能体间的协调性，常采用分布式通信框架。然而，所有智能体间的信息共享过程极为耗费资源，而采用人工预定义的通信架构又限制了智能体间的交互，从而制约了协同合作的潜力。本研究提出一种创新方法，将智能体间的通信架构概念化为可学习图。我们将该问题形式化为确定通信图的同时保持架构参数正常更新的任务，因此需要采用双层优化过程。通过利用图表示的连续松弛并融入注意力单元，本方法CommFormer能够在端到端方式下通过梯度下降高效优化通信图并同步优化架构参数。在多种协作任务上的大量实验验证了该模型在不同协作场景中的鲁棒性，无论智能体数量如何变化，智能体均能发展出更协调、更复杂的协作策略。