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The transport capacity of a communication network can be characterized by thetransition from a free-flow state to a congested state. Here, we propose adynamic routing strategy in complex networks based on hierarchical bypassselections. The routing decisions are made by the reinforcement learning agentsimplemented at selected nodes with high betweenness centrality. The learningprocesses of the agents are decoupled from each other due to the degeneracy oftheir bypasses. Through interactions mediated by the underlying trafficdynamics, the agents act cooperatively, and coherent actions arisespontaneously. With only a small number of agents, the transport capacities aresignificantly improved, including in real-world Internet networks at the routerlevel and the autonomous system level. Our strategy is also resilient to linkremovals.

Hierarchical Dynamic Routing in Complex Networks via Topologically-decoupled and Cooperative Reinforcement Learning Agents