Intelligent games meeting with multi-agent deep reinforcement learning: a comprehensive review
Yiqin Wang, Yufeng Wang, Feng Tian, Jianhua Ma, Qun Jin, Jianhua Ma, Qun Jin
Abstract
Abstract Recent years have witnessed the great achievement of the AI-driven intelligent games, such as AlphaStar defeating the human experts, and numerous intelligent games have come into the public view. Essentially, deep reinforcement learning (DRL), especially multiple-agent DRL (MADRL) has empowered a variety of artificial intelligence fields, including intelligent games. However, there is lack of systematical review on their correlations. This article provides a holistic picture on smoothly connecting intelligent games with MADRL from two perspectives: theoretical game concepts for MADRL, and MADRL for intelligent games. From the first perspective, information structure and game environmental features for MADRL algorithms are summarized; and from the second viewpoint, the challenges in intelligent games are investigated, and the existing MADRL solutions are correspondingly explored. Furthermore, the state-of-the-art (SOTA) MADRL algorithms for intelligent games are systematically categorized, especially from the perspective of credit assignment. Moreover, a comprehensively review on notorious benchmarks are conducted to facilitate the design and test of MADRL based intelligent games. Besides, a general procedure of MADRL simulations is offered. Finally, the key challenges in integrating intelligent games with MADRL, and potential future research directions are highlighted. This survey hopes to provide a thoughtful insight of developing intelligent games with the assistance of MADRL solutions and algorithms.