Observation Denoising in CYRUS Soccer Simulation 2D Team For RoboCup 2023

TL;DR

This paper introduces a denoising approach using LSTM and DNN to improve communication in the CYRUS Soccer Simulation 2D team, RoboCup 2021 champions, enhancing team performance in noisy environments.

Contribution

The paper presents a novel denoising method leveraging LSTM and DNN to enhance communication reliability in RoboCup Soccer Simulation 2D teams.

Findings

Improved communication accuracy in noisy environments

Enhanced team coordination performance

Demonstrated effectiveness on CYRUS team data

Abstract

The RoboCup competitions hold various leagues, and the Soccer Simulation 2D League is a major one among them. Soccer Simulation 2D (SS2D) match involves two teams, including 11 players and a coach, competing against each other. The players can only communicate with the Soccer Simulation Server during the game. This paper presents the latest research of the CYRUS soccer simulation 2D team, the champion of RoboCup 2021. We will explain our denoising idea powered by long short-term memory networks (LSTM) and deep neural networks (DNN). The CYRUS team uses the CYRUS2D base code that was developed based on the Helios and Gliders bases.