… problems and systems, simulators are key to … network simulators can contribute to providing
reliable and robust ML mechanisms for communications. A plethora of network simulation …

… from Reinforcement Learning (RL). To implement Vehicular Ad-hoc Network (VANET) …
Veins-Gym combines the popular Veins vehicular networking simulator with OpenAI Gym. …

… Though both studies focus on machine learning in wireless networks, … network simulators
are used to train, validate and evaluate reinforcement learning techniques in wireless networks…

… Network simulators and emulators are agile tools to investigate issues and measure …
Virtual testbeds focus on research areas such as cybersecurity, routing, machine learning, …

… with custom C++ modules to incorporate our machine learning models into the framework. To
… Fast network simulation through approximation or: How blind men can describe elephants. …

… network’s state that enables learning a broad range of data center problems. Moreover, while
labeled production data crucial for machine learning … learning with network simulators and …

… networking protocols using machine learning techniques. The proposed reinforcement learning
(… It is based on OpenAI Gym, a toolkit for RL research and ns-3 network simulator. Specifi…

… state and the corresponding parameters of the congestion window (cwnd) by interacting
with the network simulator. In the online phase, whenever an ACK is received, RemyCC looks …

… genomesized regulatory networks (eg 104 genes) in a more realistic and completely … large
GRN simulator must perform, and Section III considers relevant previous network simulation …

… In this paper, we devise the role of network simulators for bridging the … integration of simulators
in ML-aware networks for training, … In this regard, we believe that network simulators can …