用深度强化学习自动评估游戏生成内容效果，提升测试效率与准确性。
A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents
- 引入DRL智能体自动化测试不同生成策略的游戏环境。
- 基于遗传算法生成的关卡使智能体胜率最高达97%，显著优于随机生成的94%。
- 适合研究游戏生成、自动化测试或教育游戏开发的团队参考。
严肃游戏（SGs）正转向在开发中融入程序化内容生成（PCG），以提供个性化和增强的玩家体验。然而，构建评估PCG集成影响的框架仍具挑战性。本文提出一种结合深度强化学习（DRL）游戏测试智能体的自动化评估方法。为验证该框架，部署了一个包含卡牌机制的已有严肃游戏，其中引入三种不同版本的非玩家角色（NPC）生成方式：版本1为随机生成，版本2和3采用遗传算法。通过模拟常规游戏流程测试，结果显示，在版本2和3中训练的DRL智能体胜率峰值达97%，显著高于版本1的94%（p=0.009）。结果表明，该框架能有效生成有意义的数据，用于评估严肃游戏中程序化生成内容的质量。
原文摘要 · Abstract (English)
Serious Games (SGs) are nowadays shifting focus to include procedural content generation (PCG) in the development process as a means of offering personalized and enhanced player experience. However, the development of a framework to assess the impact of PCG techniques when integrated into SGs remains particularly challenging. This study proposes a methodology for automated evaluation of PCG integration in SGs, incorporating deep reinforcement learning (DRL) game testing agents. To validate the proposed framework, a previously introduced SG featuring card game mechanics and incorporating three different versions of PCG for nonplayer character (NPC) creation has been deployed. Version 1 features random NPC creation, while versions 2 and 3 utilize a genetic algorithm approach. These versions are used to test the impact of different dynamic SG environments on the proposed framework's agents. The obtained results highlight the superiority of the DRL game testing agents trained on Versions 2 and 3 over those trained on Version 1 in terms of win rate (i.e. number of wins per played games) and training time. More specifically, within the execution of a test emulating regular gameplay, both Versions 2 and 3 peaked at a 97% win rate and achieved statistically significant higher (p=0009) win rates compared to those achieved in Version 1 that peaked at 94%. Overall, results advocate towards the proposed framework's capability to produce meaningful data for the evaluation of procedurally generated content in SGs.
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