# Gliders2d: Source Code Base for RoboCup 2D Soccer Simulation League

arXiv: https://arxiv.org/abs/1812.10202 · HTML: https://mlsanity.com/paper/1812.10202/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Dec 2018.

## Built on by (most cited first, 7 of 7)

- [Pyrus Base: An Open Source Python Framework for the RoboCup 2D Soccer Simulation](https://mlsanity.com/paper/2307.16875/) (2023)
- [Engineering Features to Improve Pass Prediction in Soccer Simulation 2D Games](https://mlsanity.com/paper/2401.03410/) (2024)
- [Observation Denoising in CYRUS Soccer Simulation 2D Team For RoboCup 2023](https://mlsanity.com/paper/2305.19283/) (2023)
- [Improving Dribbling, Passing, and Marking Actions in Soccer Simulation 2D Games Using Machine Learning](https://mlsanity.com/paper/2401.03406/) (2024)
- [Cross Language Soccer Framework: An Open Source Framework for the RoboCup 2D Soccer Simulation](https://mlsanity.com/paper/2406.05621/) (2024)
- [CYRUS Soccer Simulation 2D Team Description Paper 2022](https://mlsanity.com/paper/2205.10953/) (2022)
- [Observation Denoising in CYRUS Soccer Simulation 2D Team For RoboCup 2024](https://mlsanity.com/paper/2406.05623/) (2024)

## Abstract (arXiv)

We describe Gliders2d, a base code release for Gliders, a soccer simulation team which won the RoboCup Soccer 2D Simulation League in 2016. We trace six evolutionary steps, each of which is encapsulated in a sequential change of the released code, from v1.1 to v1.6, starting from agent2d-3.1.1 (set as the baseline v1.0). These changes improve performance by adjusting the agents' stamina management, their pressing behaviour and the action-selection mechanism, as well as their positional choice in both attack and defense, and enabling riskier passes. The resultant behaviour, which is sufficiently generic to be applicable to physical robot teams, increases the players' mobility and achieves a better control of the field. The last presented version, Gliders2d-v1.6, approaches the strength of Gliders2013, and outperforms agent2d-3.1.1 by four goals per game on average. The sequential improvements demonstrate how the methodology of human-based evolutionary computation can markedly boost the overall performance with even a small number of controlled steps.