… In many cases, traditional traffic engineering (TE) approaches fail to address the quality of
service (QoS) requirements of modern networks. In recent years, deep reinforcement learning (…

… trate network traffic among … deep Reinforcement Learning (deep-RL) algorithms to overcome
the limitations of the baseline approaches. Specifically, we implement three kinds of deep…

… dynamic, designing a good Traffic Engineering (TE) policy becomes difficult due to the
complexity of solving the optimal traffic scheduling problem. Deep Reinforcement Learning (DRL) …

… Traffic Engineering (TE) policy becomes difficult due to the complexity of solving the optimal
traffic … Therefore, all the traffic engineering scheme will try to transmit the traffic through the …

… deep learning approach and the traditional reinforcement learning approach has created an
advanced approach called deep reinforcement … problems, including traffic congestion. This …

… the changes of the traffic matrix and network dynamics… Reinforcement Learning), a
Reinforcement Learning-based scheme that learns a policy to select critical flows for each given …

… node deployment and traffic paths simultaneously. Besides, traffic variation is also considered
and we use a representative Traffic Matrix (TM) to epitomize the traffic characteristics over …

… We propose a traffic signal control system which takes advantage of this new, high … deep
reinforcement learning methods to build a truly adaptive traffic signal control agent in the traffic …

… Another line of approaches uses reinforcement learning (RL) [13] which does not need …
choose deep reinforcement learning (DRL), which is powerful by combining RL and deep neural …

… This paper is focused on traffic signal control since the impact of congestions is … traffic
networks. The most used machine learning methodology for traffic signal control is Reinforcement …