将规则代理与强化学习结合，提升战场模拟中智能体的适应性与可靠性。
A Hierarchical Hybrid AI Approach: Integrating Deep Reinforcement Learning and Scripted Agents in Combat Simulations
- 分层架构：规则代理处理战术决策，强化学习负责战略规划。
- 混合方法在复杂场景中表现更优，兼顾稳定与自适应能力。
- 适合需高可靠性和动态应对能力的军事仿真系统开发。
在支持兵棋推演的战场模拟领域，智能体开发长期依赖规则驱动的脚本化方法，而深度强化学习（RL）方法近期才被引入。尽管脚本代理在受控环境中具备可预测性和一致性，但在动态、复杂的场景中因固有的僵化性表现不足。相反，强化学习代理虽具备良好的适应性和学习能力，能有效应对突发情况，却面临决策过程黑箱化及大规模仿真环境中的可扩展性问题。本文提出一种新型分层混合人工智能方法，融合脚本代理的可靠性与强化学习的动态适应能力。通过分层结构，将脚本代理用于日常战术决策，强化学习代理负责高层战略决策，从而克服各自局限并发挥优势。实验表明，该集成方法显著提升整体性能，为复杂仿真环境中智能体的构建与训练提供了一种稳健、灵活且高效的解决方案。
原文摘要 · Abstract (English)
In the domain of combat simulations in support of wargaming, the development of intelligent agents has predominantly been characterized by rule-based, scripted methodologies with deep reinforcement learning (RL) approaches only recently being introduced. While scripted agents offer predictability and consistency in controlled environments, they fall short in dynamic, complex scenarios due to their inherent inflexibility. Conversely, RL agents excel in adaptability and learning, offering potential improvements in handling unforeseen situations, but suffer from significant challenges such as black-box decision-making processes and scalability issues in larger simulation environments. This paper introduces a novel hierarchical hybrid artificial intelligence (AI) approach that synergizes the reliability and predictability of scripted agents with the dynamic, adaptive learning capabilities of RL. By structuring the AI system hierarchically, the proposed approach aims to utilize scripted agents for routine, tactical-level decisions and RL agents for higher-level, strategic decision-making, thus addressing the limitations of each method while leveraging their individual strengths. This integration is shown to significantly improve overall performance, providing a robust, adaptable, and effective solution for developing and training intelligent agents in complex simulation environments.
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