This article presents a technique for trajectory planning based on parameterized high-level
actions. These high-level actions are subtrajectories that have variable shape and duration …

We present a deep reinforcement learning (deep RL) algorithm that consists of learning-
based motion planning and imitation to tackle challenging control problems. Deep RL has …

This paper aims to examine the potential of using the emerging deep reinforcement learning
techniques in missile guidance applications. To this end, a Markovian decision process that …

This dissertation explores a novel method of solving low-thrust spacecraft targeting
problems using reinforcement learning. A reinforcement learning algorithm based on Deep …

Reinforcement learning (RL) may enable fixedwing unmanned aerial vehicle (UAV)
guidance to achieve more agile and complex objectives than typical methods. However, RL …

Unmanned aerial vehicles (UAVs) are experiencing a rapid expansion in their applications
across various domains, including goods delivery, video capturing, and traffic control. The …

This paper aims to examine the potential of using the emerging deep reinforcement learning
techniques in flight control. Instead of learning from scratch, we suggest to leverage domain …

In this paper, we take Unmanned Aerial Vehicles (UAVs) as the mobile devices to study the
problem of autonomous navigation since UAVs have been adopted as intelligent vehicles …

Current state-of-the-art model-based reinforcement learning algorithms use trajectory
sampling methods, such as the Cross-Entropy Method (CEM), for planning in continuous …

This paper introduces a novel technique, named deep guidance, that leverages deep
reinforcement learning, a branch of artificial intelligence, that enables guidance strategies to …