论文标题
复杂网络中的分层动态路由通过拓扑结构和合作的增强学习剂
Hierarchical Dynamic Routing in Complex Networks via Topologically-decoupled and Cooperative Reinforcement Learning Agents
论文作者
论文摘要
通信网络的运输能力的特征是从自由流州到拥挤状态的过渡。在这里，我们根据层次旁路选择提出了复杂网络中的动态路由策略。路由决策是由在中间性较高的部分节点上实施的强化学习代理做出的。由于旁路的堕落性，代理人的学习过程彼此分离。通过基础交通动力学介导的互动，代理商会自发地采取行动，并自发地产生连贯的动作。只有少数代理商，运输能力得到了显着提高，包括在路由器级别和自主系统级别的实际互联网网络中。我们的策略也有弹性地链接删除。
The transport capacity of a communication network can be characterized by the transition from a free-flow state to a congested state. Here, we propose a dynamic routing strategy in complex networks based on hierarchical bypass selections. The routing decisions are made by the reinforcement learning agents implemented at selected nodes with high betweenness centrality. The learning processes of the agents are decoupled from each other due to the degeneracy of their bypasses. Through interactions mediated by the underlying traffic dynamics, the agents act cooperatively, and coherent actions arise spontaneously. With only a small number of agents, the transport capacities are significantly improved, including in real-world Internet networks at the router level and the autonomous system level. Our strategy is also resilient to link removals.
