CYRUS Soccer Simulation 2D Team Description Paper 2022
This work addresses performance optimization for soccer simulation teams in RoboCup competitions, representing an incremental improvement in a specific domain.
The paper tackles the problem of improving team performance in the Soccer Simulation 2D League by introducing a Pass Prediction Deep Neural Network to enhance Unmarking Decisioning and Positioning, resulting in an increased winning rate for the CYRUS team as demonstrated in experimental results.
Soccer Simulation 2D League is one of the major leagues of RoboCup competitions. In a Soccer Simulation 2D (SS2D) game, two teams of 11 players and one coach compete against each other. The players are only allowed to communicate with the server that is called Soccer Simulation Server. This paper introduces the previous and current research of the CYRUS soccer simulation team, the champion of RoboCup 2021. We will present our idea about improving Unmarking Decisioning and Positioning by using Pass Prediction Deep Neural Network. Based on our experimental results, this idea proven to be effective on increasing the winning rate of Cyrus against opponents.