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

Computationally efficient algorithms for large-scale black-box optimization have become
increasingly important in recent years due to the growing complexity of engineering and …

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 Multi-Agent Reinforcement Learning (MARL) has demonstrated its effectiveness in
learning collaboration, but it often struggles with low-quality reward signals and high non …

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 …

Reinforcement learning is about learning agent models that make the best sequential
decisions in unknown environments. In an unknown environment, the agent needs to …

Despite significant progress in challenging problems across various domains, applying state-
of-the-art deep reinforcement learning (RL) algorithms remains challenging due to their …

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 …