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.

我们提出了一个去噪算法，利用预测建模和交叉分析来提高 Soccer Simulation 2D 中观测数据的准确性，并减少噪音和部分数据的影响，从而提高游戏性能。

CYRUS 足球模拟 2D 团队中的观察降噪技术（RoboCup 2024）

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

Soccer Simulation 2D (SS2D) is a simulation of a real soccer game in two
dimensions. In soccer, passing behavior is an essential action for keeping the
ball in possession of our team and creating goal opportunities. Similarly, for
SS2D, predicting the passing behaviors of both opponents and our teammates
helps manage resources and score more goals. Therefore, in this research, we
have tried to address the modeling of passing behavior of soccer 2D players
using Deep Neural Networks (DNN) and Random Forest (RF). We propose an embedded
data extraction module that can record the decision-making of agents in an
online format. Afterward, we apply four data sorting techniques for training
data preparation. After, we evaluate the trained models' performance playing
against 6 top teams of RoboCup 2019 that have distinctive playing strategies.
Finally, we examine the importance of different feature groups on the
prediction of a passing strategy. All results in each step of this work prove
our suggested methodology's effectiveness and improve the performance of the
pass prediction in Soccer Simulation 2D games ranging from 5\% (e.g., playing
against the same team) to 10\% (e.g., playing against Robocup top teams).

使用深度神经网络和随机森林模型，研究了 Soccer Simulation 2D 游戏中足球 2D 球员的传球行为，并验证了该方法对提高传球预测性能的有效性。

改进足球模拟 2D 游戏中的传球预测的工程特征

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

The RoboCup competitions hold various leagues, and the Soccer Simulation 2D
League is a major among them. Soccer Simulation 2D (SS2D) match involves two
teams, including 11 players and a coach for each team, competing against each
other. The players can only communicate with the Soccer Simulation Server
during the game. Several code bases are released publicly to simplify team
development. So researchers can easily focus on decision-making and
implementing machine learning methods. SS2D actions and behaviors are only
partially accurate due to different challenges, such as noise and partial
observation. Therefore, one strategy is to implement alternative denoising
methods to tackle observation inaccuracy. Our idea is to predict opponent
positions while they have yet to be seen in a finite number of cycles using
machine learning methods to make more accurate actions such as pass. We will
explain our position prediction idea powered by Long Short-Term Memory models
(LSTM) and Deep Neural Networks (DNN). The results show that the LSTM and DNN
predict the opponents' position more accurately than the standard algorithm,
such as the last-seen method.

我们通过使用长短期记忆模型（LSTM）和深度神经网络（DNN）预测对手的位置，以实现比传统算法更准确的行动，例如传球。结果表明，LSTM 和 DNN 比最后可见方法更准确地预测了对手的位置。

在部分观测环境中去噪对手位置

Denoising Opponents Position in Partial Observation Environment

Soccer, also known as football in some parts of the world, involves two teams
of eleven players whose objective is to score more goals than the opposing
team. To simulate this game and attract scientists from all over the world to
conduct research and participate in an annual computer-based soccer world cup,
Soccer Simulation 2D (SS2D) was one of the leagues initiated in the RoboCup
competition. In every SS2D game, two teams of 11 players and one coach connect
to the RoboCup Soccer Simulation Server and compete against each other. Over
the past few years, several C++ base codes have been employed to control
agents' behavior and their communication with the server. Although C++ base
codes have laid the foundation for the SS2D, developing them requires an
advanced level of C++ programming. C++ language complexity is a limiting
disadvantage of C++ base codes for all users, especially for beginners. To
conquer the challenges of C++ base codes and provide a powerful baseline for
developing machine learning concepts, we introduce Pyrus, the first Python base
code for SS2D. Pyrus is developed to encourage researchers to efficiently
develop their ideas and integrate machine learning algorithms into their teams.
Pyrus base is open-source code, and it is publicly available under MIT License
on GitHub

Soccer Simulation 2D is a RoboCup competition that has employed C++ base codes for controlling agents' behavior, but to overcome the complexity and encourage researchers, Pyrus, the first Python base code for SS2D, is introduced, providing a powerful baseline to integrate machine learning algorithms.

梨基础：一个面向 RoboCup 2D 足球模拟的开源 Python 框架

Pyrus Base: An Open Source Python Framework for the RoboCup 2D Soccer  Simulation

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.

介绍 CYRUS 足球模拟 2D 团队的最新研究，包括基于 LSTM 和 DNN 的去噪方法，并使用基于 Helios 和 Gliders 的 CYRUS2D 基础代码。