Improving Dribbling, Passing, and Marking Actions in Soccer Simulation
  2D Games Using Machine Learning
Nader Zare Omid Amini Aref Sayareh Mahtab Sarvmaili Arad Firouzkouhi Stan Matwin Amilcar Soares
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.