# Observation Denoising in CYRUS Soccer Simulation 2D Team For RoboCup 2024

arXiv: https://arxiv.org/abs/2406.05623 · HTML: https://mlsanity.com/paper/2406.05623/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 12 references that are in the map.
- First posted on arXiv 9 Jun 2024.

## Builds on (most cited first, 6 of 6)

- [Gliders2d: Source Code Base for RoboCup 2D Soccer Simulation League](https://mlsanity.com/paper/1812.10202/) (2018)
- [Cyrus2D base: Source Code Base for RoboCup 2D Soccer Simulation League](https://mlsanity.com/paper/2211.08585/) (2022)
- [Cyrus 2D Simulation Team Description Paper 2016](https://mlsanity.com/paper/2202.03726/) (2022)
- [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)

## Abstract (arXiv)

In the Soccer Simulation 2D environment, accurate observation is crucial for effective decision making. However, challenges such as partial observation and noisy data can hinder performance. To address these issues, we propose a denoising algorithm that leverages predictive modeling and intersection analysis to enhance the accuracy of observations. Our approach aims to mitigate the impact of noise and partial data, leading to improved gameplay performance. This paper presents the framework, implementation, and preliminary results of our algorithm, demonstrating its potential in refining observations in Soccer Simulation 2D. Cyrus 2D Team is using a combination of Helios, Gliders, and Cyrus base codes.