RoboCup Soccer Simulation - Development and evaluation of complex algorithms

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Supervisor:
Dávid Zoltán
Department of Automation and Applied Informatics

By means of the soccer matches between robots, researchers and students alike are seeking answers to the different issues of robotics and artificial intelligence. The problems arising during a soccer game are essentially the same as those faced in the case of robots applied in many different aspects of life. The wide-spread popularity of these matches therefore contribute to improvements in the aforementioned fields, as well.

The RoboCup soccer simulation is a game of emulating real, physical robots in a virtual gaming space, where the competing robots are controlled entirely by software solutions, therefore providing a higher abstraction level for the researchers in the field of artificial intelligence.

The challenge of performing tasks in the simulated environment is further increased by the need to design a real time and multi-agent system, the stochastic operation, the lack of information about the environment, and the exceptionally large state space. On the other hand, it provides a valuable opportunity to investigate problems arising in real life.

This thesis paper offers an insight into the simulation environment and the requirements for soccer simulation players. I present several algorithms for solving fundamental problems concerning the capabilities of the robotic soccer players. I also introduce my training module for testing the implemented capabilities.

Furthermore, I examine the machine learning methods used in implementing more advanced features, then I explain an algorithm for intercepting the ball, which I have elaborated using the Q-learning method of reinforcement learning.

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