用强化学习实现更精准的敌方威胁评估
Reinforcement Learning-based Threat Assessment
- 将威胁评估转化为强化学习问题，自动学习属性优先级
- 通过训练构建神经网络评估器，融合多维属性与自身状态
- 适用于复杂战场环境中的动态威胁判断，适合游戏与军事仿真
在部分游戏场景中，由于敌方单位数量不确定以及各类属性优先级不明确，对敌方单位威胁程度的评估与筛选成为难题，核心难点在于如何合理设定不同属性的优先级以实现威胁的量化评估。本文创新性地将威胁评估问题转化为强化学习任务，通过系统的强化学习训练，成功构建了一个高效的神经网络评估器。该评估器不仅能全面整合敌方单位的多维度属性特征，还能有效结合我方状态信息，从而实现更准确、科学的威胁评估。
原文摘要 · Abstract (English)
In some game scenarios, due to the uncertainty of the number of enemy units and the priority of various attributes, the evaluation of the threat level of enemy units as well as the screening has been a challenging research topic, and the core difficulty lies in how to reasonably set the priority of different attributes in order to achieve quantitative evaluation of the threat. In this paper, we innovatively transform the problem of threat assessment into a reinforcement learning problem, and through systematic reinforcement learning training, we successfully construct an efficient neural network evaluator. The evaluator can not only comprehensively integrate the multidimensional attribute features of the enemy, but also effectively combine our state information, thus realizing a more accurate and scientific threat assessment.
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