ELA：用于零和博弈离线学习的被利用水平增强
ELA: Exploited Level Augmentation for Offline Learning in Zero-Sum Games
AI总结：
针对零和博弈中对手策略导致结果差异显著的问题，提出ELA方法，用无监督学习估计轨迹被利用水平并增强离线学习，以识别优势策略并提升模仿学习与离线强化学习效果。
摘要：
离线学习因其能够无需直接与环境交互，从专家示范者收集的离线数据集中推导出有效策略而得到广泛应用。近期研究通过考虑数据集的特征（例如专业水平或多个示范者），探索了多种提升离线学习效率的方法。然而，在零和博弈场景中需要一种不同的方法，因为结果会随对手策略而显著变化。在本研究中，我们提出一种新方法，利用无监督学习技术，从由不同示范者生成的零和博弈离线数据集中估计每条轨迹的被利用水平。随后，我们将估计出的被利用水平纳入离线学习，以最大化优势策略的影响。我们的方法能够在多种零和博弈中实现可解释的被利用水平估计，并有效识别优势策略数据。此外，我们的被利用水平增强离线学习显著改进了面向零和博弈的原始离线学习算法，包括模仿学习和离线强化学习。
英文摘要：
Offline learning has become widely used due to its ability to derive effective policies from offline datasets gathered by expert demonstrators without interacting with the environment directly. Recent research has explored various ways to enhance offline learning efficiency by considering the characteristics (e.g., expertise level or multiple demonstrators) of the dataset. However, a different approach is necessary in the context of zero-sum games, where outcomes vary significantly based on the strategy of the opponent. In this study, we introduce a novel approach that uses unsupervised learning techniques to estimate the exploited level of each trajectory from the offline dataset of zero-sum games made by diverse demonstrators. Subsequently, we incorporate the estimated exploited level into the offline learning to maximize the influence of the dominant strategy. Our method enables interpretable exploited level estimation in multiple zero-sum games and effectively identifies dominant strategy data. Also, our exploited level augmented offline learning significantly enhances the original offline learning algorithms including imitation learning and offline reinforcement learning for zero-sum games.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
