用强化学习提升游戏中的威胁评估效率，比规则方法成功率高17.84%。
Eval-PPO: Building an Efficient Threat Evaluator Using Proximal Policy Optimization
- 将威胁评估转为强化学习任务，用PPO算法联合多维敌方特征与友军状态训练
- 在游戏场景中平均成功率提升17.84%，显著优于传统规则方法
- 适合游戏AI、智能决策系统研发者参考，尤其关注动态目标评估场景
在多种游戏场景中，从多个敌方单位中选定固定数量目标是一项极具挑战性的任务。其难点源于敌方单位威胁等级与其特征之间的复杂关系，使得基于规则的评估器设计困难。此外，传统监督学习在该问题上面临训练时缺乏明确标签的困境。本文将威胁评估重新定义为强化学习任务，提出基于近端策略优化（PPO）的高效评估器训练算法Eval-PPO。Eval-PPO通过系统化融合多维敌方特征与友军单位状态信息，实现精准威胁评估。相比规则基方法，Eval-PPO在平均成功率达到显著提升，增幅达17.84%。
原文摘要 · Abstract (English)
In various game scenarios, selecting a fixed number of targets from multiple enemy units is an extremely challenging task. This difficulty stems from the complex relationship between the threat levels of enemy units and their feature characteristics, which complicates the design of rule-based evaluators. Moreover, traditional supervised learning methods face the challenge of lacking explicit labels during training when applied to this threat evaluation problem. In this study, we redefine the threat evaluation problem as a reinforcement learning task and introduce an efficient evaluator training algorithm, Eval-PPO, based on the Proximal Policy Optimization (PPO) algorithm. Eval-PPO integrates multidimensional enemy features and the state information of friendly units through systematic training, thereby achieving precise threat assessment. Compared with rule-based methods, Eval-PPO demonstrates a significant improvement in average success rate, with an increase of 17.84%.
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