A Conceptual Framework for Performance Evaluation and Classification of Multi-Agent Hybrid Game Problems Based on Reinforcement Learning: Impact Factors and Evaluation Metrics

Multi-agent reinforcement learning (MARL) method for hybrid game is characterized by the coexistence of cooperativeness and competitiveness. This method has become more applicable for their complexity and wide applications in fields of architectural optimization, urban resource scheduling, and intelligent environmental control. However, the absence of a systematic framework for MARL algorithms’ assessment limits practical deployment in such fields. To address this concern, this paper intends to conduct a critical literature review to delve into the decisive factors of the scenarios and further develop conceptual framework for the subdivision classification, algorithm design and evaluation of hybrid games. The core research goal is to expound the differentiated classification system of hybrid game and the key determinants that significantly affect the performance of multi-agent reinforcement learning algorithms. Therefore, this paper proposes an evaluation conceptual framework for classification of hybrid games based on objectives and application scenarios, providing a reference for developers to select and design appropriate MARL algorithms and evaluate their performance according to different types of cooperative games.