In this review, we compare methods for temporal sequence learning (TSL) across the
disciplines machine-control, classical conditioning, neuronal models for TSL as well as …

The identification of recurrent driver mutations in genes encoding tyrosine kinases has
resulted in the development of molecularly-targeted treatment strategies designed to …

Advances in deep reinforcement learning have allowed autonomous agents to perform well
on Atari games, often outperforming humans, using only raw pixels to make their decisions …

Many traditional reinforcement-learning algorithms have been designed for problems with
small finite state and action spaces. Learning in such discrete problems can been difficult …

Vehicular-ad hoc networks (VANETs) hold great importance because of their potentials in
road safety improvement, traffic monitoring, and in-vehicle infotainment services. Due to high …

Reinforcement learning (RL) enables agents to take decision based on a reward function.
However, in the process of learning, the choice of values for learning algorithm parameters …

Multirobot collaboration has great potentials in tasks, such as reconnaissance and
surveillance. In this paper, we propose a multirobot system that integrates reinforcement …

Wireless sensor and actuator networks are widely adopted in various applications such as
critical infrastructure monitoring where sensory data in big volumes and velocity are prone to …

There have been tremendous improvements in deep learning and reinforcement learning
techniques. Automating learning and intelligence to the full extent remains a challenge. The …

Learning in real-world domains often requires to deal with continuous state and action
spaces. Although many solutions have been proposed to apply Reinforcement Learning …