Future Internet involves several emerging technologies such as 5G and beyond 5G
networks, vehicular networks, unmanned aerial vehicle (UAV) networks, and Internet of …

Off-policy deep reinforcement learning (RL) has been successful in a range of challenging
domains. However, standard off-policy RL algorithms can suffer from several issues, such as …

Network slicing (NS) is becoming an essential element of service management and
orchestration in communication networks, starting from mobile cellular networks and …

Successfully navigating a complex environment to obtain a desired outcome is a difficult
task, that up to recently was believed to be capable only by humans. This perception has …

In temporal-difference reinforcement learning algorithms, variance in value estimation can
cause instability and overestimation of the maximal target value. Many algorithms have been …

Empirical design in reinforcement learning is no small task. Running good experiments
requires attention to detail and at times significant computational resources. While compute …

This paper provides a theoretical study of deep neural function approximation in
reinforcement learning (RL) with the $\epsilon $-greedy exploration under the online setting …

A deep Q network (DQN)(Mnih et al.,) is an extension of Q learning, which is a typical deep
reinforcement learning method. In DQN, a Q function expresses all action values under all …

Deep reinforcement learning (DRL), which highly depends on the data representation, has
shown its potential in many practical decision-making problems. However, the process of …

This paper proposes a new robust update rule of target network for deep reinforcement
learning (DRL), to replace the conventional update rule, given as an exponential moving …