用强化版老虎机算法，快速找出每类游戏中表现最好的智能体。
Best Agent Identification for General Game Playing
- 将多任务选优转为多臂老虎机问题，用置信区间优化选择策略。
- 在两个主流游戏平台测试中，平均遗憾降低，出错概率显著减少。
- 适合高计算成本的多任务评估场景，提升智能体筛选效率。
我们提出一种高效且通用的方法，用于在多问题领域中准确识别每个子任务中表现最佳（或接近最佳）的算法。该方法将问题建模为多臂老虎机中的最优臂识别问题，其中每个老虎机对应一个具体任务，每条臂对应一个特定算法或智能体。我们设计了一种基于选定置信区间的乐观选择过程，按各臂影响整体简单遗憾的潜力对所有任务中的臂进行排序。我们在两个最流行的通用游戏博弈框架——通用视频游戏人工智能（GVGAI）框架和Ludii通用游戏系统——上评估了该方法，目标是在有限试验次数下为每款游戏选出高性能智能体。与以往多臂老虎机的最优臂识别算法相比，我们的结果在平均简单遗憾和平均错误概率方面均有显著提升。该新方法可显著提高通用游戏框架及其他高算法运行时开销多任务领域的智能体评估质量和准确性。
原文摘要 · Abstract (English)
We present an efficient and generalised procedure to accurately identify the best (or near best) performing algorithm for each sub-task in a multi-problem domain. Our approach treats this as a set of best arm identification problems for multi-armed bandits, where each bandit corresponds to a specific task and each arm corresponds to a specific algorithm or agent. We propose an optimistic selection process based on a chosen confidence interval, that ranks each arm across all bandits in terms of their potential to influence our overall simple regret. We evaluate the performance of our approach on two of the most popular general game playing domains, the General Video Game AI (GVGAI) framework and the Ludii general game playing system, with the goal of selecting a high-performing agent for each game using a limited number of available trials. Compared to previous best arm identification algorithms for multi-armed bandits, our results demonstrate a substantial performance improvement in terms of average simple regret and average probability of error. This novel approach can be used to significantly improve the quality and accuracy of agent evaluation procedures for general game frameworks, as well as other multi-task domains with high algorithm runtimes.
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