Reinforcement learning (RL), which is a class of machine learning, provides a framework by
which a system can learn from its previous interactions with its environment to efficiently …

The dynamicity of distributed wireless networks caused by node mobility, dynamic network
topology, and others has been a major challenge to routing in such networks. In the …

Reinforcement learning means learning a policy-a mapping of observations into actions-
based on feedback from the environment. The learning can be viewed as browsing a set of …

OpenAI Gym is a toolkit for reinforcement learning (RL) research. It includes a large number
of well-known problems that expose a common interface allowing to directly compare the …

Recent advances in Deep Reinforcement Learning (DRL) techniques are providing a
dramatic improvement in decision-making and automated control problems. As a result, we …

In wireless networks, context awareness and intelligence are capabilities that enable each
host to observe, learn, and respond to its complex and dynamic operating environment in an …

Recently, we have seen a boom of attempts to improve the operation of networking protocols
using machine learning techniques. The proposed reinforcement learning (RL) based …

This paper describes the Q-routing algorithm for packet routing, in which a reinforcement
learning module is embedded into each node of a switching network. Only local …

With an exponential increase in network traffic demands requiring quality of services, the
need for routing optimization has become more prominent. Recently, the advent of software …

This paper describes and evaluates the Dual Reinforcement Q-Routing algorithm DRQ-
Routing for adaptive packet routing in communication networks. Each node in the network …