Spatial Reuse (SR) has recently gained attention to maximize the performance of IEEE
802.11 Wireless Local Area Networks (WLANs). Decentralized mechanisms are expected to …

Abstract The effectiveness of Wi-Fi networks is greatly influenced by the optimization of
contention window (CW) parameters. Unfortunately, the conventional approach employed …

Flying ad-hoc networks (FANET) are one of the most important branches of wireless ad-hoc
networks, consisting of multiple unmanned air vehicles (UAVs) performing assigned tasks …

Machine Learning approaches such as Reinforcement Learning (RL) can be used to solve
problems such as spectrum sensing and channel allocation in the cognitive radio domain …

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 …

In many real-world problems, an agent simultaneously optimizes multiple rewards [7, 35,
40]. Further, more often than not, the objectives can be conflicting. Typical examples for such …

Despite the great empirical success of deep reinforcement learning, its theoretical
foundation is less well understood. In this work, we make the first attempt to theoretically …

In cognitive radio systems, fast and efficient spectrum selection is a vital task to minimize the
overhead of spectrum scanning, and hence to improve the response time of the system. So …

Balancing the ratio between exploration and exploitation is an important problem in
reinforcement learning. This paper evaluates four different exploration strategies combined …

Offline reinforcement learning proposes to learn policies from large collected datasets
without interacting with the physical environment. These algorithms have made it possible to …