  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.
