Okrepitveno učenje agentov za igranje iger v pogonu Unity : magistrsko delo

Banko, Jan

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Title:	Okrepitveno učenje agentov za igranje iger v pogonu Unity : magistrsko delo
Authors:	ID Banko, Jan (Author) ID Strnad, Damjan (Mentor) More about this mentor... ID Kohek, Štefan (Comentor)
Files:	MAG_Banko_Jan_2021.pdf (1,04 MB) MD5: 3810EF09BE2C2E4258C058D984F69B2C PID: 20.500.12556/dkum/f2dcb1b3-f075-4fe7-88ee-9ccbdb9b9f98
Language:	Slovenian
Work type:	Master's thesis/paper
Typology:	2.09 - Master's Thesis
Organization:	FERI - Faculty of Electrical Engineering and Computer Science
Abstract:	V magistrskem delu obravnavamo algoritme okrepitvenega učenja na primeru igranja računalniških iger. Namen magistrskega dela je implementacija igre v okolju Unity in analiza učinkovitosti algoritmov okrepitvenega učenja računalniškega igralca. Opisane so teoretične osnove okrepitvenega učenja, podrobneje pa so predstavljeni algoritmi PPO (angl. Proximal Policy Optimization), SAC (angl. Soft Actor Critic) in DQN (angl. Deep Q-Network), ki so uporabljeni v končni analizi. Rezultati so pokazali, da je bilo učenje agenta v celoti gledano uspešno. V testnem okolju se je najbolje odrezal algoritem PPO, z uporabo katerega je naučen agent v povprečju dosegal 86,4% maksimalne možne nagrade, najslabše pa algoritem DQN, ki ni primeren za uporabo v implementiranem testnem okolju.
Keywords:	okrepitveno učenje, računalniške igre, Unity, agent, strojno učenje
Place of publishing:	Maribor
Place of performance:	Maribor
Publisher:	[J. Banko]
Year of publishing:	2021
Number of pages:	VIII, 53 str.
PID:	20.500.12556/DKUM-79190
UDC:	004.85:004.96(043.2)
COBISS.SI-ID:	67936771
Publication date in DKUM:	17.06.2021
Views:	1246
Downloads:	158
Metadata:
Categories:	KTFMB - FERI
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Licences

License:	CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International

Link:	http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:	The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:	24.05.2021

Secondary language

Language:	English
Title:	Reinforcement learning of game-playing agents in the Unity engine
Abstract:	In the master thesis we deal with the reinforcement learning algorithms on the example of playing computer games. The purpose of the thesis is to implement a game in the Unity engine and perform an effectiveness analysis of reinforcement learning algorithms of a computer player. Theoretic bases of reinforcement learning are described and PPO (Proximal Policy Optimization), SAC (Soft Actor Critic) and DQN (Deep Q-Network) algorithms that are used in the final analysis are presented in detail. The results have shown that the learning of the agent was overall successful. The best algorithm in the test environment was PPO, using which the agent achieved 86,4% of the maximal possible reward on average, and the worst was DQN, which is not suitable for use in the implemented test environment.
Keywords:	reinforcement learning, computer games, Unity, agent, machine learning

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