Our goal is to investigate whether discrete state space algorithms are a viable solution to
continuous alternatives for mapless navigation. To this end we present an approach based …

In the last years, neural networks achieved groundbreaking successes in a wide variety of
applications. However, for safety critical tasks, such as robotics and healthcare, it is …

We propose a novel benchmark environment for Safe Reinforcement Learning focusing on
aquatic navigation. Aquatic navigation is an extremely challenging task due to the non …

The combination of Evolutionary Algorithms (EAs) and Deep Reinforcement Learning (DRL)
has been recently proposed to merge the benefits of both solutions. Existing mixed …

This work investigates the effects of Curriculum Learning (CL)-based approaches on the
agent's performance. In particular, we focus on the safety aspect of robotic mapless …

We consider the problem of Safe Deep Reinforcement Learning (DRL) using formal
verification in a trajectory generation task. In more detail, we propose an approach to verify …

ABSTRACT We consider Deep Reinforcement Learning (DRL) approaches to devise
mapless navigation strategies for mobile platforms. We propose a Genetic Deep …

Nowadays, meta-heuristic and machine learning algorithms are often used for a variety of
tasks in cloud computing operations. The choice of hyper-parameter values has a direct …

Groundbreaking successes have been achieved by Deep Reinforcement Learning (DRL) in
solving practical decision-making problems. Robotics, in particular, can involve high-cost …

Abstract A Deep Reinforcement Learning (DRL) agent tries to learn a policy maximizing a
long-term objective by trials and errors in large state spaces. However, this learning …