Exploring multi-agent learning solutions in modern tabletop games: a survey

Games play a key role in human learning by actively engaging people, posing hands-on challenges within a safe and competitive environment. Their interactive components foster skills such as negotiation, strategic thinking, and adaptation to unforeseen events which are key elements for tackling complex challenges. Artificial agents facing non-trivial real-world problems require similar skills, thus tabletop games present a beneficial training environment posing a unique set of challenges that can enhance the performance of artificial agents. This survey provides a systematic and comprehensive review of state-of-the-art training and testing environments implementing tabletop games proposed in recent years, together with the main challenges that were explored, and the Reinforcement Learning approaches implemented to tackle these problems. We begin by outlining how this survey differs from other existing reviews and highlight the main contributions it brings. Then, we analyze and categorize the training environments by different criteria. For each category identified based on the type of interactions that occur between agents, case studies are presented highlighting the modeled problems and algorithms used to enhance the agents’ performance emphasizing the main advantages and disadvantages. Finally, we examine emerging trends, potential applications to other domains, and open challenges, showcasing potential future research directions.