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 …

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

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 …

In recent years, various companies have started to shift their data services from traditional
data centers to the cloud. One of the major motivations is to save on operational costs with …

Abstract Deep Reinforcement Learning (DRL) has achieved remarkable success in perfect
information games. However, when applied to imperfect information games like Contract …

Exploration under sparse rewards is a key challenge for multi-agent reinforcement learning
problems. Although population-based learning shows its potential in producing diverse …

The integration of Evolutionary Algorithm (EA) and Reinforcement Learning (RL) has
emerged as a promising approach for tackling some challenges in RL, such as sparse …

With the advancement of the global economy, there is a growing focus on distributed
manufacturing. This study addresses the complex challenges posed by the distributed …

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