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

The primary challenge in the development of large-scale artificial intelligence (AI) systems
lies in achieving scalable decision-making—extending the AI models while maintaining …

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 …

Recent advancements in reinforcement learning have witnessed remarkable achievements
by intelligent agents ranging from game-playing to industrial applications. Of particular …

Communication plays a crucial role in information sharing within the field of multi-agent
reinforcement learning (MARL). However, how to transmit information that meets individual …

Effective multi-intersection collaboration is pivotal for reinforcement-learning-based traffic
signal control to alleviate congestion. Existing work mainly chooses neighboring …

In multi-agent reinforcement learning (MARL), information fusion through relationship
modeling can effectively learn behavior strategies. However, the high dynamics among …

Multiple unmanned aerial vehicles (Multi-UAV) systems have recently demonstrated
significant advantages in some real-world scenarios, but the limited communication range of …

Recently, structure learning of directed acyclic graphs (DAGs) has been formulated as a
continuous optimization problem by leveraging an algebraic characterization of acyclicity …

Artificial Intelligence is being employed by humans to collaboratively solve complicated
tasks for search and rescue, manufacturing, etc. Efficient teamwork can be achieved by …