… Reinforcement Learning (DRL) techniques to cope with the limitations of RL-based routing
… In this paper, we take a further step towards the goal of an efficient and intelligent routing …

… In this paper, we demonstrate the advantage of deep reinforcement learning in routing …
-optimal routing strategy for the network once trained. Compared to traditional routing algorithms, …

… investigate the routing problem of packet networks through multiagent reinforcement learning
(… In specific, the routing problem is modeled as a networked multiagent partially observable …

… reinforcement learning hierarchical optimization scheduling algorithm considers both vehicle
routing … multi-agent reinforcement learning hierarchical optimization scheduling algorithm …

… At the same time, this framework introduces, to the best of our knowledge, the first use of
reinforcement learning for frameworks specialized in solving combinatorial optimization …

… , multiagent coordination, and hierarchical memory for addressing … of reinforcement learning
in a hierarchical setting. … development of reinforcement learning in a hierarchical setting. …

… However, the conventional routing schemes cannot learn from … paper proposes deep
reinforcement learning-based routing (DRL-… Secondly, we propose a routing scheme with …

… and learn from past search sessions. Specifically, the contributions of this paper include: (1)
a reinforcement learning based approach for agents to acquire satisfactory routing policies …

… the classic problem of finding an optimal parent node in the tree-based routing. Specifically,
we propose an enhanced tree-based routing protocol based on reinforcement learning (RL); …

… In this paper, we have proposed a proactive VNE algorithm relying on hierarchical reinforcement
learning (HRL), which takes the long-term impact of VNR embedding into consideration…