Tehran2D Team Description Paper
Sadra Khanjari1
Sharif University of Technology, Tehran, Iransadra.khanjari@gmail.com

Abstract. Soccer Simulation 2D is one of the most popular and participated leagues of RoboCup, A yearly competition where soccer is used
as a subject for research and challenge. In this paper we will show theresearch and developments of Tehran2D, by implementing a block algorithm and researching what other teams have been working on.
Keywords: RoboCup · Soccer Simulation 2D · Block1Introduction

RoboCup is annual competition since 1997 where the best of artificial intelligenceand cutting edge of technology in a battle of robotics. The final goal of the league
is to reach human level robotics and artificial intelligence abilities of a humanby 2050, and to have a realistic soccer match between robots and humans.
One of the major leagues of this competition, is the RoboCup Soccer 2D
Simulation, in which 11 simulated agents (and a coach) from each team playa game of soccer in a top-down, two-dimensional view, where each agent has
limited observation of a noisy world.
The team Tehran2D was formed this year, and our current main goal was tocatch up on what the other teams are doing by reading other Team Description
Papers, and to start by developing a simple algorithm for blocking. As a basefor the team, we decided to use the CYRUS 2014 [1][2] release as it already has
many basic actions implemented.

2

Related WorkIn the recent years, there have been many developments and breakthroughs inSoccer 2D Simulation League, so we decided to study how the three top teams ofRoboCup2021 (CYRUS, Helios2021, YuShan) have reached their achievements.
In 2015, YuShan [3] worked on optimizing the kick and block model, andanalyzed formations and chain-action and pass chain patterns, while CYRUS[4] worked on defensive decision making and communication between agentsto eliminate environmental noise. Helios 2015 and 2016 [5, 7] worked on gameanalysis via clustering using EMD algorithm. In 2016, the team at CYRUS [6]worked on shooting algorithm using neural networks, and further optimization

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of shoot via pattern recognition. YuShan, sadly, did not partake in the RoboCup
competition in 2016 and 2017.
Helios2017 [9]worked on improvement of action planning by proposing a pruning method using a clustering algorithm. Meanwhile, CYRUS2017 [8] attempted
to predict opponent behaviours using machine learning.
The team at YuShan2018 [10] worked on improving chain action, introducing an inference model to defense and using data-mining to analyze teams. In
this year, Helios [14] increased the achievement probability of planned action
sequences by improving the knowledge sharing between players. Also Cyrus2018[11] worked on predicting player’s behavior in a multi agent system using neuralnetwork.
In 2019, YuShan [15] used data-mining to optimize chain action further andalso improve shooting. In this year, Cyrus [16] used an Actor-Critic Reinforcement learning algorithm to improve defensive actions.Also Helios [17] worked on
measuring the similarity between teams by using natural language processingand clustering.
In 2021, YuShan [18] used data-mining and analysis to make profiles for eachteam. Meanwhile Cyrus [19] worked on predicting teammate ball owner’s action. Helios [20] worked on putting agents into position based on their characterizations and abilities, and also developed a new analysis tool.
also other note worthy teams such as Persepolis[12, 13], Oxsy[21, 22] andFRA[23, 24] did a lot of contribution to this league and worked on different
aspects like marks, blocks and new AI models to work within SS2D.

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Block

In soccer, blocking is a defensive action that aims to prevent the opponent fromshooting and dribbling the ball towards our goal. To achieve this goal, we firstfind the position where each agent should block, and then select the blocker
agent.
To find the block point of an agent , first we calculate the middle pointof our goalie and the goalpost further away from our goalie. Then we draw aline between this middle point and the point where the ball is kickable by theopponent. Afterwards, we calculate the projection point of the agent’s positionon this line. Now we check if the projection point is between the ball and our goal.
If not, this agent is disqualified as the blocker.To select between all candidates,
we calculate an evaluation metric based on the formula below:
eval = alpha∗P layerDistanceF romP rojection+beta∗P layerDistanceF romBall
Where Alpha and Beta are fine-tuned coefficients.
Then we find the agent with minimum evaluation value and we declare it
as the blocker agent. The blocker agent moves towards its projection point andstarts intercepting once it reaches the destination.

Tehran2D Team Description Paper

3Fig. 1: Player 1 is selected based on the formula, as the length of the red lines
is less for agent 1 than agent 2. The agent will move towards the projection,
and will then start intercepting the ball.

4Future Work

Tehran2D will begin integrating what we have learnt from reading papers into
our own code base. We will further improve the actions implemented in the base,and begin using machine learning, specifically Reinforcement Learning to improve decision making. Data-mining and and analytical view of the other teams
could also help improve performance, after establishing solid fundamentals.

References
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