AI 中文总结
该论文提出Turn-Based Combat Arena新框架，支持灵活修改游戏规则参数，高吞吐量模拟且可处理海量对局数据，经评估其能有效用于游戏平衡研究，可作为游戏设计分析与智能体训练的实用平台。
AI 中文摘要
本文是关于回合制战斗竞技场（Turn-Based Combat Arena）系列研究的首篇论文，该框架是专为支持机器学习智能体的高效训练与评估而设计的可配置回合制策略游戏框架。此框架支持灵活修改游戏规则与参数，可在多样场景中开展快速实验；其架构针对高吞吐量模拟进行了优化，单台机器每秒可支持数万场游戏，还能存储与处理数十亿条游戏对局记录。本文详细研究了游戏平衡问题，尤其是游戏单位参数的平衡，评估了多种优化方法，结果显示多种方法均收敛至可比解决方案，表明其在识别平衡游戏配置方面具有鲁棒性。这些结果表明，该框架可作为游戏设计分析与智能体训练的实用平台。
英文摘要
This paper is the first in a series on Turn-Based Combat Arena, a configurable framework for turn-based strategy games designed to support the efficient training and evaluation of machine learning agents. The proposed framework enables flexible modification of game rules and parameters, allowing rapid experimentation across diverse scenarios. Its architecture is optimized for high-throughput simulation, supporting tens of thousands of games per second and enabling the storage and processing of billions of gameplay records on a single machine. The problem of balancing the game, and particularly the parameters of game units, is investigated in detail. We evaluate several optimization approaches and show that multiple methods converge to comparable solutions, suggesting robustness in identifying balanced game configurations. These results indicate that the framework can serve as a practical platform for both game design analysis and agent training.
Comments12 pages, 3 figures, 2 tables
