The RoboCup competitions hold various leagues, and the Soccer Simulation 2D League is a major among them. Soccer Simulation 2D (SS2D) match involves two teams, including 11 players and a coach for each team, competing against each other. The players can only communicate with the Soccer Simulation Server during the game. Several code bases are released publicly to simplify team development. So researchers can easily focus on decision-making and implementing machine learning methods. SS2D actions and behaviors are only partially accurate due to different challenges, such as noise and partial observation. Therefore, one strategy is to implement alternative denoising methods to tackle observation inaccuracy. Our idea is to predict opponent positions while they have yet to be seen in a finite number of cycles using machine learning methods to make more accurate actions such as pass. We will explain our position prediction idea powered by Long Short-Term Memory models (LSTM) and Deep Neural Networks (DNN). The results show that the LSTM and DNN predict the opponents' position more accurately than the standard algorithm, such as the last-seen method.

翻译：RoboCup竞赛包含多个联赛，其中足球仿真2D联赛是重要赛事之一。足球仿真2D（SS2D）比赛由两支队伍对抗，每支队伍包含11名球员和一名教练。比赛过程中，球员仅能与足球仿真服务器进行通信。为简化团队开发，多个开源代码库已公开发布。研究者可据此专注于决策制定与机器学习方法的实现。由于噪声和部分观测等挑战，SS2D的动作和行为仅具备部分准确性。因此，一种策略是采用替代性去噪方法来处理观测不精确问题。我们的思路是：在有限周期内尚未观测到对手位置时，利用机器学习方法预测其位置，从而执行更精确的动作（如传球）。本文将阐述基于长短期记忆模型（LSTM）和深度神经网络（DNN）的位置预测方法。结果表明，LSTM和DNN对对手位置的预测精度优于标准算法（如最后观测法）。