Multiagent game decision-making method based on the learning mechanism

Reinforcement learning, a cornerstone in the expansive landscape of artificial intelligence, has asserted its dominance as the prevailing methodology in contemporary multiagent system decision-making because of its formidable efficacy. However, the path to the zenith of algorithmic excellence is fraught with challenges intrinsic to traditional multiagent reinforcement learning algorithms, such as dimensionality explosion, scarcity of training samples, and the labyrinthine nature of migration processes. In a concerted effort to surmount these formidable challenges and propel the evolution of algorithmic prowess, this paper unfurls its inquiry from the perspective of learning mechanisms and undertakes an exhaustive exploration of the symbiotic integration of learning mechanisms and reinforcement learning. At the inception of this scholarly expedition, we meticulously delineate the rudimentary principles underpinning multiagent algorithms, present a historical trajectory tracing their developmental evolution, and cast a discerning eye upon the salient challenges that have been formidable impediments in their trajectory. The ensuing narrative charts a course into the avant-garde realm of multiagent reinforcement learning methods anchored in learning mechanisms, a paradigmatic shift that emerges as an innovative frontier in the field. Among these learning mechanisms, meta-learning and transfer learning are empirically validated as useful instruments in hastening the learning trajectory of multiagent systems and simultaneously mitigating the intricate challenges posed by dimensionality explosion. This paper assumes the role of a sagacious guide through the labyrinthine landscape of multiagent reinforcement learning, focusing on the manifold applications of learning mechanisms across diverse domains. A comprehensive review delineates the impact of learning mechanisms in curriculum learning, evolutionary games, meta-learning, hierarchical learning, and transfer learning. The research outcomes within these thematic realms are methodically cataloged, with a discerning eye cast upon the limitations inherent in each methodology and erudite propositions for the trajectory of future improvements. The discourse pivots toward synthesizing advancements and accomplishments wrought by fusion algorithms in practical milieus. This paper meticulously examines the transformative impact of fusion algorithms in real-world applications, with a detailed exposition of their deployment in domains as diverse as traffic control and gaming. Simultaneously, an incisive analysis charting the future trajectory of fusion algorithms is conducted. This prediction encompasses exploring nascent theories, refining algorithmic efficacy, and expanding dissemination and application across a broader spectrum of domains. Through this scholarly odyssey, this paper provides an invaluable compass for navigating the uncharted waters of future research endeavors and the judicious deployment of multiagent reinforcement learning algorithms in pragmatic scenarios.