… In the past few years, we have witnessed significant advances in deep reinforcement learning
(DRL) … In this paper, we address the application of DRL to perform online routing in optical …

… CONCLUSION In this paper, we discussed the challenges involved in the application of
Deep Reinforcement Learning (DRL) to networking. Contrary to the dominant trend in other …

… in computer networks in which a router determines the next-hop of each packet in the queue
… Reinforcement learning (RL) has been introduced to design autonomous packet routing …

… an algorithm for routing optimization in hierarchical cognitive … be employed to design optimum
routes between the nodes thus … the performance of the routing algorithm by identifying the …

… a hierarchical routing framework that combines routing path planning via Q-learning with …
via an improved Q-learning algorithm to implement routing path selections between any two …

… This paper introduces the ReinforceRouting model, a novel approach to optimizing
evacuation routes using reinforcement learning (RL). The model incorporates a unique RL …

… Since a CM in cluster i ( s i ∈ N i ) takes multiple hops to reach CC, we define a routing matrix
for each CM, denoted by H i , s i . It is assumed that the routing matrix remains the same for …

… are time-varying, hence these learning-based routing mechanisms are required to have the
… Reinforcement Learning-based Routing schemes combined with Multi-optimality routing …

… to the routing problem by making use of reinforcement learning and other heuristics. The paper
proposes a routing … Hierarchical Q-Routing which is based on an Q-Routing. Providing an …

… intelligent multicast routing algorithm based on deep hierarchical reinforcement learning is
… internal and external controllers of hierarchical reinforcement learning based on the global …