prikaz prve stranice dokumenta Primjena neuronskih mreža u pronalasku rješenja igre
  Download
PDF 1.58 MB
master's thesis
Primjena neuronskih mreža u pronalasku rješenja igre
Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2018. urn:nbn:hr:200:122145
Brazdil, Vedran
Josip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
Department of Software Engineering
Chair of Visual Computing

Institutional repository: Faculty of Electrical Engineering, Computer Science and Information Technology Osijek

Cite this document

Brazdil, V. (2018). Primjena neuronskih mreža u pronalasku rješenja igre (Master's thesis). Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek. Retrieved from https://urn.nsk.hr/urn:nbn:hr:200:122145

Brazdil, Vedran. "Primjena neuronskih mreža u pronalasku rješenja igre." Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2018. https://urn.nsk.hr/urn:nbn:hr:200:122145

Brazdil, Vedran. "Primjena neuronskih mreža u pronalasku rješenja igre." Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2018. https://urn.nsk.hr/urn:nbn:hr:200:122145

Brazdil, V. (2018). 'Primjena neuronskih mreža u pronalasku rješenja igre', Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, accessed 02 October 2024, https://urn.nsk.hr/urn:nbn:hr:200:122145

Brazdil V. Primjena neuronskih mreža u pronalasku rješenja igre [Master's thesis]. Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek; 2018 [cited 2024 October 02] Available at: https://urn.nsk.hr/urn:nbn:hr:200:122145

V. Brazdil, "Primjena neuronskih mreža u pronalasku rješenja igre", Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Osijek, 2018. Available at: https://urn.nsk.hr/urn:nbn:hr:200:122145

Metadata
TitlePrimjena neuronskih mreža u pronalasku rješenja igre
Title (english)Application of neural networks in finding a game solution
AuthorVedran Brazdil
MentorČaslav Livada (mentor)
Committee memberČaslav Livada (predsjednik povjerenstva)
Committee memberAlfonzo Baumgartner (član povjerenstva)
Committee memberHrvoje Leventić (član povjerenstva)
GranterJosip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
(Department of Software Engineering)
(Chair of Visual Computing)
Osijek
Defense date and country2018-09-07, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Data Processing
Abstract
Unity 3D, jednostavan program za izradu igara, uz ML-Agents dodatak pruža nove usluge treniranja umjetne inteligencije da izvršava određene zadatke. Treniranje agenata ne ovisi samo o jačini CPU ili GPU, već ovisi i o okruženju koje stvorimo za agente te mehanici nagrađivanja koju napišemo u kodu. Agenti uče uz pomoć neuronske mreže kojoj mi određujemo broj ulaza, odnosno opservacija te broj izlaza, odnosno akcija koje agent izvršava. Vrlo je bitno agenta postepeno učiti od lakših pa sve do težih zadataka. Da bismo trenirali agente kroz Unity i ML-Agents dodatak, nije potrebno dodatno znanje o neuronskim mrežama zbog toga što nam funkcije neuronske mreže izvršava TensorFlow u pozadini, algoritmom PPO kojeg mi ne mijenjamo.
Abstract (english)
Unity 3D, simple program for creating games, with ML-Agents addition gives new services of training artificial intelligence to do the specific tasks. Training agents do not depend only on strength of CPU or GPU, but it depends more on the environment which we create for agents and also the mechanic of rewarding that we write in code. Agents learn throughout the help of neuron network whom we determine the number of inputs, also called observations and the number of outputs, also called actions that the agent needs to do. It is very important for the agent to be learnt step by step from very easy to very hard tasks. To train agents throughout Unity and ML-Agents, it is not necessary to know additional knowledge about neural networks because all functions of the neural network are done by TensorFlow in the background, by algorithm PPO which we do not change by hand.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:200:122145
Study programmeTitle: Graduate University Study Programme in Computer Engineering
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2018-10-17 07:36:48