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 …

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

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 …

By deploying machine learning algorithms at the network edge, edge learning recently
emerges as a promising framework to support intelligent mobile services. It effectively …

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 …

Q-learning methods are a common class of algorithms used in reinforcement learning (RL).
However, their behavior with function approximation, especially with neural networks, is …

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 …

As the scarce spectrum resource is becoming overcrowded, cognitive radio indicates great
flexibility to improve the spectrum efficiency by opportunistically accessing the authorized …

In this paper we introduce the novel paradigm of docition for cognitive radio (CR) networks.
We consider that the CRs are intelligent radios implementing a learning process through …

Deep reinforcement learning (RL) has achieved several high profile successes in difficult
decision-making problems. However, these algorithms typically require a huge amount of …