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

TL;DR

This paper introduces a denoising algorithm for Soccer Simulation 2D that improves observation accuracy by using predictive modeling and intersection analysis, enhancing team performance amid noisy and partial data.

Contribution

The paper presents a novel denoising framework specifically designed for Soccer Simulation 2D, integrating predictive modeling and intersection analysis to improve observation quality.

Findings

Enhanced observation accuracy demonstrated in preliminary results

Improved gameplay performance with denoising algorithm

Potential for better decision making in noisy environments

Abstract

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.