The RoboCup competition was started in 1997, and is known as the oldest
RoboCup league. The RoboCup 2D Soccer Simulation League is a stochastic,
partially observable soccer environment in which 24 autonomous agents play on
two opposing teams. In this paper, we detail the main strategies and
functionalities of CYRUS, the RoboCup 2021 2D Soccer Simulation League
champions. The new functionalities presented and discussed in this work are (i)
Multi Action Dribble, (ii) Pass Prediction and (iii) Marking Decision. The
Multi Action Dribbling strategy enabled CYRUS to succeed more often and to be
safer when dribbling actions were performed during a game. The Pass Prediction
enhanced our gameplay by predicting our teammate's passing behavior,
anticipating and making our agents collaborate better towards scoring goals.
Finally, the Marking Decision addressed the multi-agent matching problem to
improve CYRUS defensive strategy by finding an optimal solution to mark
opponents' players.

RoboCup 2021 2D Soccer Simulation League champions CYRUS implemented new functionalities including Multi Action Dribble and Marking Decision, while Pass Prediction improved teammate collaboration for scoring goals.

利用机器学习提升足球模拟 2D 游戏中的盘带、传球和盯人行为

Improving Dribbling, Passing, and Marking Actions in Soccer Simulation  2D Games Using Machine Learning

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

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。

CYRUS 足球仿真 2D 团队在 RoboCup 2023 中的观测去噪

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

We introduce the Adaptive Skills, Adaptive Partitions (ASAP) framework that
(1) learns skills (i.e., temporally extended actions or options) as well as (2)
where to apply them. We believe that both (1) and (2) are necessary for a truly
general skill learning framework, which is a key building block needed to scale
up to lifelong learning agents. The ASAP framework can also solve related new
tasks simply by adapting where it applies its existing learned skills. We prove
that ASAP converges to a local optimum under natural conditions. Finally, our
experimental results, which include a RoboCup domain, demonstrate the ability
of ASAP to learn where to reuse skills as well as solve multiple tasks with
considerably less experience than solving each task from scratch.

介绍了自适应技能（ASAP）框架，它能够学习技能和如何应用技能，并能够通过适应其中应用现有学习技能的位置来解决相关新任务，同时证明了其在自然条件下收敛到局部最优解的能力，相关的 RoboCup 实验也表明了它学习如何重复利用技能以及用比每个任务都从头解决消耗的经验更少的能力来解决多个任务。