Thesis Details
Posilované učení pro hraní robotického fotbalu
Goal of this thesis is creation of artificial intelligence capable of controlling robotic soccer player simulated in SimSpark environment. Agent created is expanding capabilities of existing third party agent which provides set of basic skills such as localization on the field, dribbling with the ball and omnidirectional walk. Responsibility of the created agent is to pick the best action based current state of the game. This decision making was implemented using reinforcement learning and its method Q-learning. State of the game is transformed into 2D picture with several planes. This picture is then analyzed using deep convolution neural network implemented using C++ and DeepCL library.
Machine learning, reinforcement learning, deep neural networks, convolution neural networks, Q-learning, robotic soccer, RoboCup
Burget Lukáš, doc. Ing., Ph.D. (DCGM FIT BUT), člen
Hliněná Dana, doc. RNDr., Ph.D. (DMAT FEEC BUT), člen
Matoušek Petr, doc. Ing., Ph.D., M.A. (DIFS FIT BUT), člen
Zachariášová Marcela, Ing., Ph.D. (DCSY FIT BUT), člen
@bachelorsthesis{FITBT18016,
author = "Hynek Bo\v{c}\'{a}n",
type = "Bachelor's thesis",
title = "Posilovan\'{e} u\v{c}en\'{i} pro hran\'{i} robotick\'{e}ho fotbalu",
school = "Brno University of Technology, Faculty of Information Technology",
year = 2017,
location = "Brno, CZ",
language = "czech",
url = "https://www.fit.vut.cz/study/thesis/18016/"
}