Nowadays, many research studies and industrial investigations have allowed the integration
of the Internet of Things (IoT) in current and future networking applications by deploying a …

Reinforcement learning has recently been studied in various fields and also used to
optimally control IoT devices supporting the expansion of Internet connection beyond the …

It is well known that the deadly triad of function approximation, bootstrapping, and off-policy
learning can make reinforcement learning (RL) unstable or even cause it to diverge …

The open radio access network (O-RAN) architecture supports intelligent network control
algorithms as one of its core capabilities. Data-driven applications incorporate such …

Get hands-on experience in creating state-of-the-art reinforcement learning agents using
TensorFlow and RLlib to solve complex real-world business and industry problems with the …

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 …

This article is a gentle discussion about the field of reinforcement learning in practice, about
opportunities and challenges, touching a broad range of topics, with perspectives and …

A common technique to improve learning performance in deep reinforcement learning
(DRL) and many other machine learning algorithms is to run multiple learning agents in …

Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is
beginning to show some successes in real-world scenarios. However, much of the research …

Deep reinforcement learning (RL) holds considerable promise to help address a variety of
multi-agent problems in a dynamic and complex environment. In multi-agent scenarios, most …