Due to ever-growing soccer data collection approaches and progressing artificial
intelligence (AI) methods, soccer analysis, evaluation, and decision-making have received …

Multi-agent reinforcement learning (MARL) has emerged as a useful approach to solving
decentralised decision-making problems at scale. Research in the field has been growing …

Learning from datasets without interaction with environments (Offline Learning) is an
essential step to apply Reinforcement Learning (RL) algorithms in real-world scenarios …

Grou** is ubiquitous in natural systems and is essential for promoting efficiency in team
coordination. This paper proposes a novel formulation of Group-oriented Multi-Agent …

Many advances in cooperative multi-agent reinforcement learning (MARL) are based on two
common design principles: value decomposition and parameter sharing. A typical MARL …

Cooperative multi-robot tasks can benefit from heterogeneity in the robots' physical and
behavioral traits. In spite of this, traditional Multi-Agent Reinforcement Learning (MARL) …

Multi-agent reinforcement learning (MARL) suffers from the non-stationarity problem, which
is the ever-changing targets at every iteration when multiple agents update their policies at …

Cooperative multi-agent reinforcement learning (MARL) has made prominent progress in
recent years. For training efficiency and scalability, most of the MARL algorithms make all …

This paper presents a comprehensive review of deep learning applications in the video
game industry, focusing on how these techniques can be utilized in game development …

The difficulty of appropriately assigning credit is particularly heightened in cooperative
MARL with sparse reward, due to the concurrent time and structural scales involved …