[2E1-GS-10] AI application:

[2E1-GS-10-04] Evaluation of Player Behavior in Badminton Doubles Using Deep Reinforcement Learning

Eito Nakahara¹, Keisuke Fujii², 〇Hiroaki Kawashima¹ (1. University of Hyogo, 2. Nagoya University)

Keywords:Reinforcement learning, Sport

In badminton doubles, there are no specific roles or positions such as attackers or defenders, which exist in many team sports. Therefore, it is important for each player to understand the game situation and take appropriate actions. On the other hand, previous studies have only proposed methods for evaluating players' overall performance and strokes.
In this study, we aim to evaluate whether players are able to take appropriate actions in terms of strokes and formations, based on video footage of badminton doubles matches. Specifically, we propose a method to evaluate players' actions using the action value function of deep reinforcement learning, taking as input the players' poses and shuttle positions detected from past match videos. For the evaluation of actions, we use counterfactual data in which real actions are changed into fictitious actions, and compare the results with those obtained when players performed different actions to evaluate whether players performed actions appropriate to the game situation. The experimental results confirmed that the proposed method can capture and evaluate the tendency of the ideal behavior in a game situation, and demonstrated its effectiveness.