Telecommunication systems are increasingly complex, dynamic, and heterogeneous. Tools
are needed to efficiently support and automate complex control and management …

Reinforcement learning (RL) has become more popular due to promising results in
applications such as chat-bots, healthcare, and autonomous driving. However, one …

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 …

Reinforcement Learning (RL) has continuously risen in popularity in recent years.
Consequently, multiple RL algorithms and extensions have been developed for various use …

Driven by the soaring traffic demand and the growing diversity of mobile services, wireless
networks are evolving to be increasingly dense and heterogeneous. Accordingly, in such …

Reinforcement learning (RL) is a dynamic and evolving subfield of machine learning that
focuses on training intelligent agents to learn and adapt through interactions with their …

This paper presents a comprehensive literature review on applications of deep
reinforcement learning (DRL) in communications and networking. Modern networks, eg …

This paper aims to review the advancements, limitations, and real-world applications of RL.
Additionally, it will explore the future of RL and the challenges that must be addressed to …

Reinforcement learning (RL) is a new propitious research space that is well-known
nowadays on the internet of things (IoT), media and social sensing computing are …

Reinforcement Learning (RL) has gained significant momentum in the development of
network protocols. However, RL-based protocols are still in their infancy, and substantial …