Due to the rising demand for Quality of Service (QoS) in the emerging network, this paper considers the routing problem of efficiently planning the transmission path of flow demands to balance the load of the network. Existing classical routing algorithms based on static decision-making are unable to adjust to dynamical scenarios with fluctuating flow. Traditional reinforcement learning-based methods face challenges in large-scale scenarios due to the vast state and action spaces. Besides, the neural networks used for policy generation also suffer from issues like rudimentary feature extraction and limited learning capability. In this paper, we first introduce RequestNet, a novel attention-based model designed to better leverage the information of network topology and feature correlations. We also propose a multi-agent modeling approach for the network packet routing problem, named RequestNet-MA, which treats pairs of edge routers as agents, allowing us to allocate traffic from the perspective of flow demands, thus significantly reducing the dimensions of state and action spaces and enhancing inter-agent communication and cooperation. Extensive experiments and ablation studies demonstrate that the proposed RequestNet-MA can reduce maximum link utilization and scale well to a large-scale network.

To solve collaborative tasks, Multi-Agent Systems (MAS) have been widely used because of their collaboration capabilities and flexibility. They have been applied in various fields, such as robotic collaboration and intelligent transportation systems. In multi-agent collaborative problems, coordinating the collaboration strategies between agents is the most challenging aspect. Traditional Multi-Agent Reinforcement Learning (MARL) methods attempt to transform the team objective into individual objectives for each agent through value decomposition. However, the value allocation mechanisms in existing methods struggle to accurately measure the contribution differences between agents. In this paper, we propose an MARL method based on structured coordination by leveraging the local interaction structure among agents. Depending on the application scenarios, we present explicit and implicit implementation approaches. The explicit method constructs an explicit collaboration graph based on the cooperation relationships between agents, and then uses Shapley values to assess the contribution of each agent in the collaboration graph. Additionally, we propose a method based on self-attention mechanisms to dynamically construct implicit interaction structures. Experimental results demonstrate that, compared to several state-of-the-art multi-agent collaboration algorithms, the two collaboration methods we proposed achieve significant performance advantages across multiple complex collaboration scenarios.