通过经验上下文与布朗运动对羽毛球运动员行为进行离线模仿
Offline Imitation of Badminton Player Behavior via Experiential Contexts and Brownian Motion
AI总结：
本文提出 RallyNet，一种基于上下文马尔可夫决策过程与几何布朗运动的分层离线模仿学习模型，用于从离线羽毛球单打数据中按回合模仿运动员决策行为，并在真实数据集上验证其性能优于现有方法至少16%。
摘要：
在回合制运动动态且快速的战术对抗中，羽毛球作为一种内在范式尤为突出，它要求运动员根据对手情况进行决策。尽管从离线专家数据中进行序列决策学习已在多个领域取得进展，但如何从离线羽毛球比赛中按回合（rally-wise）模仿人类运动员的行为仍未得到充分探索。复现对手行为有助于运动员在赛前进行有方向的策略发展。然而，直接应用现有方法会受到比赛固有层级结构以及运动员交替行动的回合制特性所带来的复合效应的影响。在本文中，我们提出 RallyNet，一种用于羽毛球运动员行为的新型分层离线模仿学习模型：（i）RallyNet 通过将决策过程建模为上下文马尔可夫决策过程（contextual Markov decision process）来捕捉运动员的决策依赖关系。（ii）RallyNet 利用经验生成上下文，作为智能体在回合中的意图。（iii）为了生成更真实的行为，RallyNet 利用几何布朗运动（Geometric Brownian Motion，GBM）对运动员之间的交互进行建模，为学习运动员行为引入有价值的归纳偏置。通过这种方式，RallyNet 将运动员意图与基于 GBM 的交互模型联系起来，为体育分析提供了对交互的理解。我们在最大的可用真实世界羽毛球数据集（包含男子和女子单打比赛）上对 RallyNet 进行了广泛验证，证明了其模仿运动员行为的能力。结果表明，RallyNet 优于离线模仿学习方法和最先进的回合制方法，在基于规则智能体的平均归一化得分上至少高出 16%。此外，我们还讨论了多种实际用例，以突出 RallyNet 的适用性。
英文摘要：
In the dynamic and rapid tactic involvements of turn-based sports, badminton stands out as an intrinsic paradigm that requires alter-dependent decision-making of players. While the advancement of learning from offline expert data in sequential decision-making has been witnessed in various domains, how to rally-wise imitate the behaviors of human players from offline badminton matches has remained underexplored. Replicating opponents' behavior benefits players by allowing them to undergo strategic development with direction before matches. However, directly applying existing methods suffers from the inherent hierarchy of the match and the compounding effect due to the turn-based nature of players alternatively taking actions. In this paper, we propose RallyNet, a novel hierarchical offline imitation learning model for badminton player behaviors: (i) RallyNet captures players' decision dependencies by modeling decision-making processes as a contextual Markov decision process. (ii) RallyNet leverages the experience to generate context as the agent's intent in the rally. (iii) To generate more realistic behavior, RallyNet leverages Geometric Brownian Motion (GBM) to model the interactions between players by introducing a valuable inductive bias for learning player behaviors. In this manner, RallyNet links player intents with interaction models with GBM, providing an understanding of interactions for sports analytics. We extensively validate RallyNet with the largest available real-world badminton dataset consisting of men's and women's singles, demonstrating its ability to imitate player behaviors. Results reveal RallyNet's superiority over offline imitation learning methods and state-of-the-art turn-based approaches, outperforming them by at least 16% in mean rule-based agent normalization score. Furthermore, we discuss various practical use cases to highlight RallyNet's applicability.
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。
