Improving Dribbling, Passing, and Marking Actions in Soccer Simulation 2D Games Using Machine Learning

• URL: http://arxiv.org/abs/2401.03406v1
• Date: Sun, 7 Jan 2024 07:54:26 GMT
• Title: Improving Dribbling, Passing, and Marking Actions in Soccer Simulation 2D Games Using Machine Learning
• Authors: Nader Zare, Omid Amini, Aref Sayareh, Mahtab Sarvmaili, Arad Firouzkouhi, Stan Matwin, Amilcar Soares
• Abstract summary: The RoboCup competition was started in 1997, and is known as the oldest RoboCup league. The RoboCup 2D Soccer Simulation League is a partially observable soccer environment in which 24 autonomous agents play on two opposing teams. In this paper, we detail the main strategies and functionalities of CYRUS, the RoboCup 2021 2D Soccer Simulation League champions.
• Score: 4.350850682297813
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The RoboCup competition was started in 1997, and is known as the oldest RoboCup league. The RoboCup 2D Soccer Simulation League is a stochastic, partially observable soccer environment in which 24 autonomous agents play on two opposing teams. In this paper, we detail the main strategies and functionalities of CYRUS, the RoboCup 2021 2D Soccer Simulation League champions. The new functionalities presented and discussed in this work are (i) Multi Action Dribble, (ii) Pass Prediction and (iii) Marking Decision. The Multi Action Dribbling strategy enabled CYRUS to succeed more often and to be safer when dribbling actions were performed during a game. The Pass Prediction enhanced our gameplay by predicting our teammate's passing behavior, anticipating and making our agents collaborate better towards scoring goals. Finally, the Marking Decision addressed the multi-agent matching problem to improve CYRUS defensive strategy by finding an optimal solution to mark opponents' players.

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