… routing tasks and proposed guiding suggestions for using reinforcement learning models
to solve the routing … Q-Routing model [19] showed that the RL intelligent routing of packet-level …

… reinforcement learning. Then, we introduce the HDRL for solving VRPTW by hierarchically
… not only using hierarchical structure but also considering the collaboration among agents. …

… 2.6 Hierarchical routing algorithms Hierarchical routing protocols such as [29] build a
hierarchy of nodes, typically through clustering techniques. Cluster heads (CHs) provide data …

… Learning (ML). This paper proposes REDO, a Reinforcement lEarning-based Dynamic
rOuting algorithm selection method that decides on the conventional routing algorithm to be …

… Accounting for the hierarchy of caches has become a common practice in recent works;
see also [33], [34], [35]. Joint routing and innetwork content caching in a hierarchical cache …

… compute routes; a destination could be a single computer attached to the network, a
subnetwork within an internetwork, or a routing domain within a hierarchical routing architecture. …

… a multi-agent reinforcement learning (MARL) framework for inter-domain routing that achieves
… We learn the routing model that will be deployed on each AS using MARL. The proposed …

… routing, a multi-agent reinforcement learning routing scheme for Dragonfly systems. Q-adaptive
routing enables routers to learn … leveraging advanced reinforcement learning technology. …

… routing problems, the neural network architectures used in the above works do not fully take
into account the relationship between problem entities, which is a critical property of routing …

… routes with minimal time, in this paper, we propose a novel deep reinforcement learning-…
Specifically, we transform the online routing problem to a vehicle tour generation problem, and …