Primjena podržanog učenja na automatsko igranje igara

undergraduate thesis

• Title (english): Application of reinforcement learning on automatic game playing
• Author: Matija Frandolić
• Mentor: Marko Čupić (mentor)
• Committee member: Marko Čupić (predsjednik povjerenstva)
• Committee member: Vjeko Kužina (član povjerenstva)
• Committee member: Zoran Medić (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2021-07-07, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

Podržano učenje jedan je od glavnih pristupa strojnom učenju i primjenjuje se za rješavanje mnogih zanimljivih problema. Jedna od osobito zanimljivih je primjena na automatsko igranje igara, što podrazumijeva izradu računalnih igrača koji samostalno znaju igrati neku igru. Postoje brojni algoritmi koji se primjenjuju u svrhu učenja takvih igrača, a među popularne i široko primjenjive tu se ubraja i algoritam poznat pod nazivom Q-učenje. U sklopu ovog rada izrađena je kopija poznate igre Space Invaders. Za treniranje igrača korištena je varijanta Q-učenja koja koristi umjetnu neuronsku mrežu kao aproksimacijski model. Isprobane su različite konfiguracije neprijatelja te je istražen utjecaj hiperparametara. Komentirani su konačni rezultati i dana je njihova interpretacija u kontekstu korištenih hiperparametara.

Abstract (english)

Reinforcement learning is one of the main approaches to machine learning and it is applied to solve many interesting problems. One of the particularly interesting ones is the application in automatic game playing, which involves the creation of computer players that know how to play a certain game on their own. There are a number of algorithms used to teach such players and among the popular and widely used ones is the Q-learning algorithm. A copy of the famous game Space Invaders was made as part of this paper. A variant of Q-learning, which uses an artificial neural network as an approximation model, was used to train the player. Different enemy configurations were tested. The influence of hyperparameters was also examined. Final results are commented and their interpretation is given in the context of used hyperparameters.

• Keywords: podržano učenje; Q-učenje; umjetne neuronske mreže; automatsko igranje igara
• Keywords (english): reinforcement learning; Q-learning; artificial neural networks; automatic game playing
• Language: croatian
• URN:NBN: urn:nbn:hr:168:169138
• Study programme: Title: Computing; Study programme type: university; Study level: undergraduate; Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2023-01-14 20:25:45