We have analyzed 127 publications for this review paper, which discuss applications of
Reinforcement Learning (RL) in marketing, robotics, gaming, automated cars, natural …

Unmanned aerial vehicle (UAV) navigation plays a crucial role in its ability to perform
autonomous missions in complex environments. Most of the existing reinforcement learning …

In this paper, we propose an innovative approach for the path planning of Uninhabited
Aerial Vehicles (UAVs) that combines an advanced Genetic Algorithm (GA) for optimising …

One of the biggest problems in gaming AI is related to how we can optimize and adapt a
deep reinforcement learning (DRL) model, especially when it is running inside complex …

Most methods that compute trajectories for un-or low-powered flight operate under
simplifying assumptions such as constant curve radii and wind conditions. Likewise …

This paper presents a real-time trajectory planning framework for urban air mobility (UAM)
that is both safe and scalable. The proposed framework employs a decentralized, free-flight …

Reinforcement learning (RL) has been shown to be effective in path planning. However, it
usually requires exploring a sufficient number of state–action pairs, some of which may be …

The rigorous safety verification of control systems in critical applications is essential, given
their in creasing complexity and integration into everyday life. Simulation-based falsification …