This paper puts forward a novel deep reinforcement learning control using deep
deterministic policy gradient (DRLC-DDPG) framework to address the reference tracking …

An urban pluvial flood is a devastating, costly natural disaster requiring effective rescue path
planning to mitigate the loss of lives and property. The inherent uncertainty and complexity …

This research provides a method that accelerates learning and avoids local minima to
improve the policy gradient algorithm's learning process. Reinforcement learning has the …

Navigating efficiently and safely through human crowds is essential for mobile robots in
diverse applications such as delivery services, home assistance, healthcare, and …

Reinforcement learning (RL) has demonstrated considerable potential in solving challenges
across various domains, notably in autonomous driving. Nevertheless, implementing RL in …

Exploiting additional low loss bands of optical fibres is a promising solution to expand the
capacity of optical transport networks. Recently, extended bandwidth bands (super bands) …

Modern active distribution networks (ADNs) witness increasing complexities that require
efforts in control practices, including optimal reactive power dispatch (ORPD). Deep …

Goal-conditioned Reinforcement Learning (RL) holds promise for addressing intricate
control challenges by enabling agents to learn and execute desired skills through separate …

Training novice users to operate an excavator for learning different skills requires the
presence of expert teachers. Considering the complexity of the problem, it is comparatively …

We propose an algorithm that combines Reinforcement Learning (RL) and a PID cascade
control for landing a drone on a moving unmanned ground vehicle (UGV). Unlike other …