  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.
