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The RoboCup competition was started in 1997, and is known as the oldestRoboCup league. The RoboCup 2D Soccer Simulation League is a stochastic,partially observable soccer environment in which 24 autonomous agents play ontwo opposing teams. In this paper, we detail the main strategies andfunctionalities of CYRUS, the RoboCup 2021 2D Soccer Simulation Leaguechampions. The new functionalities presented and discussed in this work are (i)Multi Action Dribble, (ii) Pass Prediction and (iii) Marking Decision. TheMulti Action Dribbling strategy enabled CYRUS to succeed more often and to besafer when dribbling actions were performed during a game. The Pass Predictionenhanced 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 toimprove CYRUS defensive strategy by finding an optimal solution to markopponents' players.

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