Hierarchical Reinforcement Learning (HRL) enables autonomous decomposition of
challenging long-horizon decision-making tasks into simpler subtasks. During the past …

Abstract Deep Reinforcement Learning has made significant progress in multi-agent
systems in recent years. The aim of this review article is to provide an overview of recent …

The advances in reinforcement learning have recorded sublime success in various domains.
Although the multi-agent domain has been overshadowed by its single-agent counterpart …

In light of the emergence of deep reinforcement learning (DRL) in recommender systems
research and several fruitful results in recent years, this survey aims to provide a timely and …

Many real-world problems, such as network packet routing and urban traffic control, are
naturally modeled as multi-agent reinforcement learning (RL) problems. However, existing …

Cooperative multi-agent systems (MAS) are ones in which several agents attempt, through
their interaction, to jointly solve tasks or to maximize utility. Due to the interactions among the …

Reinforcement learning is bedeviled by the curse of dimensionality: the number of
parameters to be learned grows exponentially with the size of any compact encoding of a …

Robotic urban search and rescue (USAR) is a challenging yet promising research area
which has significant application potentials as has been seen during the rescue and …

Over the past few years, several methods for segmenting a scene containing multiple rigidly
moving objects have been proposed. However, most existing methods have been tested on …

In the last few years, reinforcement learning (RL), also called adaptive (or approximate)
dynamic programming, has emerged as a powerful tool for solving complex sequential …