Decision-Making in Wargames: An E-CARGO Perspective

In the field of management research, complex decision-making scenarios are frequently encountered. Intelligent decision-making games serve as an essential tool for simulating such scenarios, enabling decision-makers to evaluate strategies and allocate resources more effectively. However, traditional intelligent decision-making games relying on deep reinforcement learning (DRL) often suffer from prolonged training times, convergence difficulties, and challenges in multiagent coordination. To address these limitations, this study proposes an enhanced game framework that integrates role-based collaboration (RBC) with the environment, class, agent, role, group, object (E-CARGO model). In this framework, agents are first assigned different roles, after which reinforcement learning (RL) techniques are applied for policy training. Simulation experiments conducted on the winning-first platform demonstrate that the proposed method achieves superior convergence performance and agent intelligence compared with conventional RL approaches, effectively mitigating the identified challenges and enhancing overall decision-making efficiency.