Abstract We present the Marconi-Rosenblatt Framework for Intelligent Networks (MR-iNet
Gym) an open-source architecture designed for accelerating research and development of …

There is a phenomenal burst of research activities in machine learning and wireless
systems. Machine learning evolved from a collection of powerful techniques in AI areas and …

In the conventional approaches using reinforcement learning (RL) for resource allocation in
wireless networks, the structure of the policy depends on network circumstances such as the …

ABSTRACT Q-learning is a very popular reinforcement learning algorithm being proven to
converge to optimal policies in Markov decision processes. However, Q-learning shows …

Recently, Deep Reinforcement Learning (DRL) has increasingly been used to solve
complex problems in mobile networks. There are two main types of DRL models: off-policy …

The recent development of reinforcement learning (RL) has boosted the adoption of online
RL for wireless radio resource management (RRM). However, online RL algorithms require …

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 …

This work focuses on multi-agent reinforcement learning (RL) with inter-agent
communication, in which communication is differentiable and optimized through …

This chapter discusses the use of machine learning to perform distributed resource
allocation in cognitive radio (CR) networks. There are many reinforcement learning …

In large, distributed systems, like ad-hoc networks, centralized learning of routing or
movement policies may be impractical. We need to employ learning algorithms that can …