Design and Implementation of a Tactical Wargame Adversarial Platform Based on Lightweight SDK

With the growing demand for intelligent decision-making in military simulations and adversarial training, developing flexible and scalable wargame AI platforms has become increasingly important. This paper presents the design and implementation of a tactical-level wargame AI platform built on a lightweight SDK, enabling rapid development and evaluation of intelligent agents. The platform integrates essential components—including communication handling, state abstraction, and decision arbitration—into a unified framework. Developers can deploy custom agents by implementing a single interface function, significantly lowering the development threshold. The system supports various adversarial modes, including agent-vs-agent, human-vs-agent, and hybrid simulations, and offers features such as accelerated training, local execution, and cloud-based replay storage. A standardized data structure is used to represent tactical states, command inputs, and feedback, enhancing modeling efficiency and performance assessment. While large language models (LLMs) are not yet integrated, the architecture provides reserved interfaces for future support of semantic input and strategic reasoning. Experimental results validate the platform's effectiveness in agent training and its extensibility for further research.