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

Nader Zare, Omid Amini, Aref Sayareh, Mahtab Sarvmaili, Arad Firouzkouhi +2 more
1/7/2024
cs.ROcs.AIcs.LG

No official code found. Best implementation: naderzare/CYRUS2014 (13 stars, GPL-3.0, last commit 2020-04-23).

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.

View on arXivView PDF

Code Implementations(1)

naderzare/CYRUS2014Community100%
GPL-3.0

Cyrus2014 Soccer simulation 2D team, 1st IranOpen2014, 5th Robocup2014

1311C, M4Jul 5, 20186 years agoGPL-3.0

Similar Papers

Engineering Features to Improve Pass Prediction in Soccer Simulation 2D Games

89% match2024

SoccerDiffusion: Toward Learning End-to-End Humanoid Robot Soccer from Gameplay Recordings

81% match2025

Multi-Agent Reinforcement Learning and Real-Time Decision-Making in Robotic Soccer for Virtual Environments

80% match2025

Reinforcement Learning Within the Classical Robotics Stack: A Case Study in Robot Soccer

79% match2024

Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning

78% match2023

TranSPORTmer: A Holistic Approach to Trajectory Understanding in Multi-Agent Sports

78% match2024

Cite this paper

@article{zare2024improving,
  title  = {Improving Dribbling, Passing, and Marking Actions in Soccer Simulation 2D Games Using Machine Learning},
  author = {Nader Zare and Omid Amini and Aref Sayareh and Mahtab Sarvmaili and Arad Firouzkouhi and Stan Matwin and Amilcar Soares},
  year   = {2024},
  eprint = {2401.03406},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2401.03406v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2401.03406.svg)](https://codeofpaper.com/paper/2401.03406)

Discussion