Multi-agent reinforcement learning (MARL) has been a rapidly evolving field. This paper
presents a comprehensive survey of MARL and its applications. We trace the historical …

In recent years, multi-agent reinforcement learning techniques have been widely used and
evolved in the field of artificial intelligence. However, traditional reinforcement learning …

This study proposes a logical Petri net model to leverage the modeling advantages of Petri
nets in handling batch processing and uncertainty in value passing and to integrate relevant …

Team competition in multi-agent Markov games is an increasingly important setting for multi-
agent reinforcement learning, due to its general applicability in modeling many real-life …

In this paper, we identify the best learning scenario to train a team of agents to compete
against multiple possible strategies of opposing teams. We evaluate cooperative value …

Although predictive maintenance has improved the availability of industrial systems by
predicting the remaining useful life and scheduling maintenance actions in a timely manner …

The concept of smart cities has emerged as an instrumental solution to tackle the intricate
challenges associated with urbanization in the twenty-first century. Among the myriad of …

This study proposes a logical Petri net model to leverage the modeling advantages of Petri
nets in handling batch processing and uncertainty in value passing and to integrate relevant …

To enable sophisticated interactions between humans and robots in a shared environment,
robots must infer the intentions and strategies of their human counterparts. This inference …