Reinforcement learning (RL) is a machine learning approach that trains agents to maximize
cumulative rewards through interactions with environments. The integration of RL with deep …

Reinforcement Learning (RL) has proven to be highly effective in various real-world
applications. However, in certain scenarios, Evolutionary Algorithms (EAs) have been …

Abstract We consider Reinforcement Learning (RL) problems where an agent attempts to
maximize a reward signal while minimizing a cost function that models unsafe behaviors …

Evolutionary Reinforcement Learning (ERL), which integrates Evolutionary Algorithms (EAs)
and Reinforcement Learning (RL) for optimization, has demonstrated remarkable …

Deep neuroevolution and deep Reinforcement Learning have received a lot of attention
over the past few years. Some works have compared them, highlighting their pros and cons …

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 …

Recent Multi-Agent Deep Reinforcement Learning approaches factorize a global action-
value to address non-stationarity and favor cooperation. These methods, however, hinder …

Autonomous mobile robots employed in industrial applications often operate in complex and
uncertain environments. In this paper we propose an approach based on an extension of …

Safety is essential for deploying Deep Reinforcement Learning (DRL) algorithms in real-
world scenarios. Recently, verification approaches have been proposed to allow quantifying …

Abstract The integration of Reinforcement Learning (RL) and Evolutionary Algorithms (EAs)
aims at simultaneously exploiting the sample efficiency as well as the diversity and …