… deep reinforcement learning for routing optimization) to realize the automatic routing …
An intact “collections-decision-adjustment” loop is proposed to perform an intelligent routing …

… To this end, this paper proposes NavTL, a learning-based framework to jointly control traffic
… decentralized bi-directional hierarchical reinforcement learning framework by regarding TLs …

… for routing traffic in WMNs, called Reinforcement Learning-based Distributed Routing (RLBDR),
that (1) considers the critical areas around the gateways where mesh routers are much …

… routing protocols for WSNs, according to the network structure, they can be categorized
into flat routing and hierarchical routing. Our work is aimed at designing flat routing. Flat …

… the routing under large networks, we design a hierarchical RL … design a hierarchical RL-based
routing algorithm (called Q-… Although we discuss Q-hierarchical routing for Dragonfly net…

… stability and efficiently improves the routing performance as well as the … reinforcement
learning-based mobile sink model extends the network lifetime, provides an improved learning …

… and therefore to perform fast learning. We argue that our learning approach makes the
problem feasible, and we report on a novel reinforcement learning dialogue system that performs …

… and more readily deployed than flow-based routing, where the forwarding entries could be
… emerging Reinforcement Learning (RL) to reduce the time complexity and routing update …

… Second, we use Deep Reinforcement Learning (DRL) to build agents that learn how to …
adaptive routing in multi-layer hierarchical software defined networks: A reinforcement learning …

… deep reinforcement learning framework to solve a dynamic and uncertain vehicle routing …
Besides, a cutting-edge reinforcement learning algorithm is presented to control the value …